{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "316e3288-8925-4b66-b182-8a13fed68a21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "from scipy.integrate import IntegrationWarning\n",
    "\n",
    "# Ignore integration warnings\n",
    "warnings.simplefilter(\"ignore\", IntegrationWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1bea0747-c53b-4c12-994b-3b17a3de51ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Physical baryon density parameter (\\Omega_b h^2) from Planck\n",
    "obh2pl = 0.02236\n",
    "\n",
    "# Matter density parameter (\\Omega_m) from Planck\n",
    "ompl = 0.3166\n",
    "\n",
    "# Hubble constant (H_0) in km s^-1 Mpc^-1 from Planck\n",
    "H0pl = 67.27\n",
    "\n",
    "# Dimensionless Hubble parameter (h) from Planck\n",
    "h0pl = H0pl / 100\n",
    "\n",
    "# Dimensionless Hubble parameter (h) from Type Ia Supernovae\n",
    "h0riess = 0.7403\n",
    "\n",
    "# Cosmic Microwave Background (CMB) temperature in Kelvin\n",
    "Tcmb = 2.7255\n",
    "\n",
    "# Speed of light in vacuum in km s^-1\n",
    "c = 299792.458\n",
    "\n",
    "# Hubble distance scale (c / 100) in Mpc\n",
    "cH0 = 2997.92458"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "94df8140-3980-49ef-a065-e9c5dfbaf970",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mpmath\n",
    "import numpy as np\n",
    "# w(a) = w_0 + w_a * (1 - a)^n, i.e., nCPL model\n",
    "# For n=1 we get the usual CPL model\n",
    "def w(a, w0, wa, n):\n",
    "    return w0 + wa * (1 - a)**n\n",
    "\n",
    "# f_DE(a) for the nCPL model\n",
    "def f(a, w0, wa, n):\n",
    "    if wa == 0:\n",
    "        return a**(-3 * (1 + w0))\n",
    "    \n",
    "    harmonic_n = mpmath.harmonic(n)\n",
    "    hyper_term = mpmath.hyper([1, 1, 1 - n], [2, 2], a)\n",
    "    term1 = a**(-3 * (1 + w0 + wa))\n",
    "    term2 = mpmath.exp(-3 * wa * harmonic_n + 3 * a * n * wa * hyper_term)\n",
    "    return float(term1 * term2)\n",
    "# Define z_equality and a_equality in order to avoid some problems.\n",
    "# Tcmb is added as a parameter with a default value of 2.725 K.\n",
    "def zeq(om, h, Tcmb=2.725):\n",
    "    return 2.5*10**4 * om * (h**2) * (Tcmb / 2.7)**(-4)\n",
    "\n",
    "def aeq(om, h, Tcmb=2.725):\n",
    "    return 1.0 / (1.0 + zeq(om, h, Tcmb))\n",
    "\n",
    "#humble parameter\n",
    "def H(a, om, w0, wa, n, h):\n",
    "    # matter and radiation term\n",
    "    term_m_r = (a**-3) * om * (1 + aeq(om, h) / a)\n",
    "    # dark energy term\n",
    "    term_de = (1 - om * (1 + aeq(om, h))) * f(a, w0, wa, n)\n",
    "    \n",
    "    return 100 * h * np.sqrt(term_m_r + term_de)\n",
    "\n",
    "#sound speed\n",
    "def cs(a, obh2):\n",
    "    R = 31500 * obh2 * (Tcmb / 2.7)**-4 * a\n",
    "    return c / np.sqrt(3 * (1 + R))\n",
    "\n",
    "#redshift at decoupling\n",
    "def zcmb(om, obh2, h):\n",
    "    omh2 = om * h**2\n",
    "    g1 = (0.0783 * obh2**-0.238) / (1 + 39.5 * obh2**0.763)\n",
    "    g2 = 0.560 / (1 + 21.1 * obh2**1.81)\n",
    "    return 1048 * (1 + 0.00124 * obh2**-0.738) * (1 + g1 * omh2**g2)\n",
    "\n",
    "#drag redshift\n",
    "def zdrag(om, obh2, h):\n",
    "    omh2 = om * h**2\n",
    "    b1 = 0.313 * omh2**-0.419 * (1 + 0.607 * omh2**0.674)\n",
    "    b2 = 0.238 * omh2**0.223\n",
    "    \n",
    "    term1 = 1291 * omh2**0.251 / (1 + 0.659 * omh2**0.828)\n",
    "    term2 = 1 + b1 * obh2**b2\n",
    "    return term1 * term2\n",
    "\n",
    "from scipy.integrate import quad\n",
    "\n",
    "#comoving sound horizon at drag redshift\n",
    "def rs(ze, om, obh2, w0, wa, n, h):\n",
    "    lower_limit = 10**-11\n",
    "    upper_limit = 1.0 / (1.0 + ze)\n",
    "    \n",
    "    def integrand(x):\n",
    "        return cs(x, obh2) / (x**2 * H(x, om, w0, wa, n, h))\n",
    "    \n",
    "    \n",
    "    result, error = quad(integrand, lower_limit, upper_limit)\n",
    "    \n",
    "    return result\n",
    "    \n",
    "#luminosity distance\n",
    "from scipy.integrate import solve_ivp\n",
    "from functools import lru_cache\n",
    "\n",
    "@lru_cache(maxsize=None)\n",
    "def DLsol(om, w0, wa, n, h):\n",
    "    def dy_dz(zz, y):\n",
    "        a = 1.0 / (1.0 + zz)\n",
    "        return 1.0 / (H(a, om, w0, wa, n, h) / (100.0 * h))\n",
    "    sol = solve_ivp(dy_dz, [0, 1300], [0.0], dense_output=True)\n",
    "    return sol.sol\n",
    "\n",
    "def DL(z, om, w0, wa, n, h):\n",
    "    sol_func = DLsol(om, w0, wa, n, h)\n",
    "    y_val = sol_func(z)[0]\n",
    "    dL_val = y_val * (1.0 + z)\n",
    "    return (c / (100.0 * h)) * dL_val\n",
    "    \n",
    "#angular diameter distance\n",
    "def DA(z, om, w0, wa, n, h):\n",
    "    return DL(z, om, w0, wa, n, h) / (1.0 + z)**2\n",
    "    \n",
    "#volume averaged distance\n",
    "def Dv(zbao, om, w0, wa, n, h):\n",
    "    dl_term = DL(zbao, om, w0, wa, n, h) / (1.0 + zbao)\n",
    "    h_val = H(1.0 / (1.0 + zbao), om, w0, wa, n, h)\n",
    "    \n",
    "    return ((dl_term**2) * (c * zbao) / h_val)**(1.0 / 3.0)\n",
    "\n",
    "#BAO dz ratio\n",
    "def dz(zbao, om, obh2, w0, wa, n, h):\n",
    "    term1 = rs(zdrag(om, obh2, h), om, obh2, w0, wa, n, h)\n",
    "    term2 = Dv(zbao, om, w0, wa, n, h)\n",
    "    return term1/term2\n",
    "\n",
    "#DH distance\n",
    "def DH(z, om, w0, wa, n, h):\n",
    "    return c/H(1/(1+z), om, w0, wa, n, h)\n",
    "\n",
    "def hz(z, om, w0, wa, n, h):\n",
    "    return H(1/(1+z), om, w0, wa, n, h)\n",
    "\n",
    "#shift parameter\n",
    "def R(om, obh2, w0, wa, n, h):\n",
    "    term1 = np.sqrt(om*(100*h)**2) * DA(zcmb(om, obh2, h), om, w0, wa, n, h)\n",
    "    term2 = 1 + zcmb(om, obh2, h)\n",
    "    return term1*term2/c\n",
    "\n",
    "#angular scale of sound horizon at recombination\n",
    "def la(om, obh2, w0, wa, n, h):\n",
    "    term1 = np.pi * DA(zcmb(om, obh2, h), om, w0, wa, n, h)\n",
    "    term2 = 1 + zcmb(om, obh2, h)\n",
    "    term3 = rs(zcmb(om, obh2, h), om, obh2, w0, wa, n, h)\n",
    "    return term1*term2/term3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "45f23398-1779-4457-9707-2ddf87ec3406",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data combination\n",
    "#CMB data\n",
    "datacmb = np.array([1.74963, 301.80845, 0.02237])\n",
    "\n",
    "# Covariance Matrix for {R, l_a, Omega_b h^2}\n",
    "covcmb = 10**-8 * np.array([\n",
    "    [1598.9554,  17112.007,  -36.311179],\n",
    "    [17112.007,  811208.45,  -494.79813],\n",
    "    [-36.311179, -494.79813,  2.1242182]\n",
    "])\n",
    "\n",
    "# Inverse Covariance Matrix\n",
    "invcov = np.linalg.inv(covcmb)\n",
    "\n",
    "# Number of CMB data points\n",
    "ndatCMB = len(datacmb)\n",
    "\n",
    "# Vector of residuals (Model - Data)\n",
    "def vec(om, obh2, w0, wa, n, h):\n",
    "    return np.array([\n",
    "        R(om, obh2, w0, wa, n, h) - datacmb[0],\n",
    "        la(om, obh2, w0, wa, n, h) - datacmb[1],\n",
    "        obh2 - datacmb[2]\n",
    "    ])\n",
    "\n",
    "# chi^2 function for CMB shift parameters\n",
    "def chi2R(om, obh2, w0, wa, n, h):\n",
    "    v = vec(om, obh2, w0, wa, n, h)\n",
    "    return v.T @ invcov @ v\n",
    "\n",
    "#ΒΑΟ data\n",
    "databao = np.array([\n",
    "    [0.106, 0.336, 0.015],\n",
    "    [0.44, 0.073, 0.031],\n",
    "    [0.6, 0.0726, 0.0164],\n",
    "    [0.73, 0.0592, 0.0185]\n",
    "])\n",
    "\n",
    "\n",
    "Cijbaoinv = np.array([\n",
    "    [1 / (databao[0, 2]**2), 0, 0, 0],\n",
    "    [0, 1040.3, -807.5, 336.8],\n",
    "    [0, -807.5, 3720.3, -1551.9],\n",
    "    [0, 336.8, -1551.9, 2914.9]\n",
    "])\n",
    "\n",
    "def vecbao(om, obh2, w0, wa, n, h):\n",
    "    return np.array([\n",
    "        databao[i, 1] - dz(databao[i, 0], om, obh2, w0, wa, n, h)\n",
    "        for i in range(len(databao))\n",
    "    ])\n",
    "\n",
    "#SDSS\n",
    "dataSDSS = np.array([\n",
    "    [0.15, 4.465666824, 0.1681350461],\n",
    "    [0.32, 8.4673, 0.167],\n",
    "    [0.57, 13.7728, 0.134],\n",
    "])\n",
    "\n",
    "#Ly-a\n",
    "dataLya = np.array([\n",
    "    [2.34, 11.2005988024, 0.56],\n",
    "    [2.34, 8.86, 0.29],\n",
    "])\n",
    "\n",
    "CijLyainv = np.array([\n",
    "    [1 / (dataLya[0, 2]**2), 0 ],\n",
    "    [0, 1 / (dataLya[1, 2]**2)]\n",
    "])\n",
    "\n",
    "def vecLya(om, obh2, w0, wa, n, h):\n",
    "    return np.array([\n",
    "        dataLya[0, 1] - DA(dataLya[0, 0], om, w0, wa, n, h) / rs(zdrag(om, obh2, h), om, obh2, w0, wa, n, h),\n",
    "        dataLya[1, 1] - DH(dataLya[1, 0], om, w0, wa, n, h) / rs(zdrag(om, obh2, h), om, obh2, w0, wa, n, h)\n",
    "    ])\n",
    "\n",
    "ndatBAO = len(dataSDSS) + len(databao) + len(dataLya)\n",
    "\n",
    "#total BAO χ^2\n",
    "def chi2bao(om, obh2, w0, wa, n, h):\n",
    "    v_bao = vecbao(om, obh2, w0, wa, n, h)\n",
    "    v_lya = vecLya(om, obh2, w0, wa, n, h)\n",
    "    \n",
    "    term_bao = v_bao.T @ Cijbaoinv @ v_bao\n",
    "    \n",
    "    term_sdss = sum(\n",
    "        ((dataSDSS[i, 1] - 1 / dz(dataSDSS[i, 0], om, obh2, w0, wa, n, h))**2) / (dataSDSS[i, 2]**2)\n",
    "        for i in range(len(dataSDSS))\n",
    "    )\n",
    "    \n",
    "    term_lya = v_lya.T @ CijLyainv @ v_lya\n",
    "    \n",
    "    return term_bao + term_sdss + term_lya"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9dc43e25-253d-4396-98d5-5d6b9eefabe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#full pantheon data\n",
    "import pandas as pd\n",
    "dataPanth = pd.read_csv(\"lcparam_full_long_zhel.txt\", sep=r'\\s+')\n",
    "ndatPanth = len(dataPanth)\n",
    "Dij0 = np.loadtxt(\"sys_full_long.txt\")\n",
    "Dij = Dij0[1:].reshape((1048,1048))\n",
    "Id = np.ones(ndatPanth)\n",
    "#Construct of the covariance using only the statistical uncertainties\n",
    "Covstat = np.diag(dataPanth.iloc[:, 5]**2)\n",
    "#Construct of the covariance using only the systematic uncertainties\n",
    "Covsys = Dij\n",
    "Cijtot = Covsys + Covstat\n",
    "InvCovTotal = np.linalg.inv(Cijtot)\n",
    "InvCovstat = np.linalg.inv(Covstat)\n",
    "\n",
    "# χ^2 for the pantheon data\n",
    "def chi2Panth(M, om, w0, wa, n, h):\n",
    "    zcmb = dataPanth.iloc[:, 1].values  \n",
    "    zhel = dataPanth.iloc[:, 2].values  \n",
    "    mb = dataPanth.iloc[:, 4].values\n",
    "    dl_vals = DL(zcmb, om, w0, wa, n, h)\n",
    "    delta_m = mb - (M + 5 * np.log10(((1 + zhel) / (1 + zcmb)) * dl_vals) + 25)\n",
    "    chi2 = delta_m.T @ InvCovTotal @ delta_m\n",
    "    return chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "85f045f4-cb9f-402b-8401-f36e92b1ac0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Mathematica point in Python: 1032.8152046282437\n",
      "1026.259730262297\n",
      "0.12038195926556611\n",
      "6.43509240668118\n"
     ]
    }
   ],
   "source": [
    "#total χ^2\n",
    "def chi2total(M, om, w0, wa, h):\n",
    "    return chi2R(om, obh2pl, w0, wa, 1.0, h) + chi2bao(om, obh2pl, w0, wa, 1.0, h) + chi2Panth(M, om, w0, wa, 1.0, h)\n",
    "ndatot = ndatCMB + ndatBAO + ndatPanth\n",
    "test_val = chi2total(-19.4294, 0.316911, -1, 0, 0.672379)\n",
    "print(\"Test Mathematica point in Python:\", test_val)\n",
    "x_val = chi2Panth(-19.4294, 0.316911, -1.0, 0.0, 1.0, 0.672379)\n",
    "print(x_val)\n",
    "y_val = chi2R(0.316911, obh2pl, -1.0, 0.0, 1, 0.672379)\n",
    "print(y_val)\n",
    "z_val = chi2bao(0.316911, obh2pl, -1.0, 0.0, 1, 0.672379)\n",
    "print(z_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "468fb5c8-49ef-4df6-b1c1-051717d798de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi2: 1032.7906234691407\n",
      "Mlcdm = -19.428583683208007\n",
      "omlcdm = 0.31658931659913897\n",
      "wolcdm = -1\n",
      "walcdm = 0\n",
      "hlcdm = 0.6725515844159159\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.optimize import basinhopping\n",
    "\n",
    "def obj_lcdm(params):\n",
    "    M, om, h = params\n",
    "    if om <= 0.01 or om >= 0.99 or h <= 0.1 or h >= 1.5:\n",
    "        return 1e10\n",
    "    try:\n",
    "        val = chi2total(M, om, -1, 0, h)\n",
    "        if np.isnan(val):\n",
    "            return 1e10\n",
    "        return val\n",
    "    except:\n",
    "        return 1e10\n",
    "\n",
    "initial_guess = [-19.05, 0.3205, 0.6705]\n",
    "\n",
    "minimizer_kwargs = {\n",
    "    \"method\": \"Powell\",\n",
    "    \"bounds\": [(None, None), (0.01, 0.99), (0.1, 1.5)],\n",
    "    \"options\": {'ftol': 1e-12, 'xtol': 1e-12}\n",
    "}\n",
    "\n",
    "minlcdm = basinhopping(\n",
    "    obj_lcdm,\n",
    "    x0=initial_guess,\n",
    "    minimizer_kwargs=minimizer_kwargs,\n",
    "    niter=10,\n",
    "    stepsize=0.05,\n",
    "    T=1.0\n",
    ")\n",
    "\n",
    "Mlcdm = minlcdm.x[0]\n",
    "omlcdm = minlcdm.x[1]\n",
    "wolcdm = -1\n",
    "walcdm = 0\n",
    "hlcdm = minlcdm.x[2]\n",
    "\n",
    "print(\"Minimum chi2:\", minlcdm.fun)\n",
    "print(\"Mlcdm =\", Mlcdm)\n",
    "print(\"omlcdm =\", omlcdm)\n",
    "print(\"wolcdm =\", wolcdm)\n",
    "print(\"walcdm =\", walcdm)\n",
    "print(\"hlcdm =\", hlcdm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b6c78a3e-c928-4b4d-981e-c7bc375ed75a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi2: 1032.6923784195606\n",
      "Mwwcdm = -19.423397527757963\n",
      "omwwcdm = 0.3144839134036616\n",
      "wowwcdm = -1.0103536564754707\n",
      "wawwcdm = 0\n",
      "hwwcdm = 0.6751045284002756\n"
     ]
    }
   ],
   "source": [
    "#χ^2 for the wCDM model using all three likelihoods\n",
    "def obj_wwcdm(params):\n",
    "    M, om, w0, h = params\n",
    "    if om <= 0.01 or om >= 0.99 or w0 <= -3.0 or w0 >= 1.0 or h <= 0.1 or h >= 1.5:\n",
    "        return 1e10\n",
    "    try:\n",
    "        val = chi2total(M, om, w0, 0, h)\n",
    "        if np.isnan(val):\n",
    "            return 1e10\n",
    "        return val\n",
    "    except:\n",
    "        return 1e10\n",
    "\n",
    "initial_guess_wwcdm = [-19.05, 0.3205, -1.0, 0.6705]\n",
    "\n",
    "minimizer_kwargs_wwcdm = {\n",
    "    \"method\": \"Powell\",\n",
    "    \"bounds\": [(None, None), (0.01, 0.99), (-3.0, 1.0), (0.1, 1.5)],\n",
    "    \"options\": {'ftol': 1e-12, 'xtol': 1e-12}\n",
    "}\n",
    "\n",
    "minwwcdm = basinhopping(\n",
    "    obj_wwcdm,\n",
    "    x0=initial_guess_wwcdm,\n",
    "    minimizer_kwargs=minimizer_kwargs_wwcdm,\n",
    "    niter=10,\n",
    "    stepsize=0.05,\n",
    "    T=1.0\n",
    ")\n",
    "\n",
    "Mwwcdm = minwwcdm.x[0]\n",
    "omwwcdm = minwwcdm.x[1]\n",
    "wowwcdm = minwwcdm.x[2]\n",
    "wawwcdm = 0\n",
    "hwwcdm = minwwcdm.x[3]\n",
    "\n",
    "print(\"Minimum chi2:\", minwwcdm.fun)\n",
    "print(\"Mwwcdm =\", Mwwcdm)\n",
    "print(\"omwwcdm =\", omwwcdm)\n",
    "print(\"wowwcdm =\", wowwcdm)\n",
    "print(\"wawwcdm =\", wawwcdm)\n",
    "print(\"hwwcdm =\", hwwcdm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a7623817-8077-4064-be73-428ce19321a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding Riess point \n",
    "def chi2riessh(h):\n",
    "    return (h - h0riess)**2/(0.0142)**2\n",
    "\n",
    "def chi2total5(M, om, w0, wa, h):\n",
    "    return chi2R(om, obh2pl, w0, wa, 1.0, h) + chi2bao(om, obh2pl, w0, wa, 1.0, h) + chi2Panth(M, om, w0, wa, 1.0, h) + chi2riessh(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "52b53a45-587b-43fd-a3b7-e4a921297031",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum chi2: 1047.8763051813366\n",
      "Mwcdm = -19.385712877623565\n",
      "omwcdm = 0.29837620160467354\n",
      "wowcdm = -1.0740747639231654\n",
      "wawcdm = 0\n",
      "hwcdm = 0.6934669332795699\n"
     ]
    }
   ],
   "source": [
    "#χ^2 for the wCDM model using all three likelihoods and adding the Riess point\n",
    "def obj_wcdm(params):\n",
    "    M, om, w0, h = params\n",
    "    if om <= 0.01 or om >= 0.99 or w0 <= -3.0 or w0 >= 1.0 or h <= 0.1 or h >= 1.5:\n",
    "        return 1e10\n",
    "    try:\n",
    "        val = chi2total5(M, om, w0, 0, h)\n",
    "        if np.isnan(val):\n",
    "            return 1e10\n",
    "        return val\n",
    "    except:\n",
    "        return 1e10\n",
    "\n",
    "initial_guess_wcdm = [-19.05, 0.3205, -1.0, 0.6705]\n",
    "\n",
    "minimizer_kwargs_wcdm = {\n",
    "    \"method\": \"Powell\",\n",
    "    \"bounds\": [(None, None), (0.01, 0.99), (-3.0, 1.0), (0.1, 1.5)],\n",
    "    \"options\": {'ftol': 1e-12, 'xtol': 1e-12}\n",
    "}\n",
    "\n",
    "minwcdm = basinhopping(\n",
    "    obj_wcdm,\n",
    "    x0=initial_guess_wcdm,\n",
    "    minimizer_kwargs=minimizer_kwargs_wcdm,\n",
    "    niter=10,\n",
    "    stepsize=0.05,\n",
    "    T=1.0\n",
    ")\n",
    "\n",
    "Mwcdm = minwcdm.x[0]\n",
    "omwcdm = minwcdm.x[1]\n",
    "wowcdm = minwcdm.x[2]\n",
    "wawcdm = 0\n",
    "hwcdm = minwcdm.x[3]\n",
    "\n",
    "print(\"Minimum chi2:\", minwcdm.fun)\n",
    "print(\"Mwcdm =\", Mwcdm)\n",
    "print(\"omwcdm =\", omwcdm)\n",
    "print(\"wowcdm =\", wowcdm)\n",
    "print(\"wawcdm =\", wawcdm)\n",
    "print(\"hwcdm =\", hwcdm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "02ca9b4b-eeeb-4803-aaea-8abc0da27c21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "chi2_lcdm = 1032.7906234691416\n",
    "Mlcdm = -19.428583683289137\n",
    "omlcdm = 0.3165893182993782\n",
    "wolcdm = -1\n",
    "walcdm = 0\n",
    "hlcdm = 0.6725515834715047\n",
    "\n",
    "chi2_wwcdm = 1032.6923784195612\n",
    "Mwwcdm = -19.423397513557912\n",
    "omwwcdm = 0.31448390671613147\n",
    "wowwcdm = -1.0103536947470049\n",
    "wawwcdm = 0\n",
    "hwwcdm = 0.6751045370485488\n",
    "\n",
    "\n",
    "\n",
    "#με Riess\n",
    "chi2_wcdm = 1047.8763051813366\n",
    "Mwcdm = -19.385712877623565\n",
    "omwcdm = 0.29837620160467354\n",
    "wowcdm = -1.0740747639231654\n",
    "wawcdm = 0\n",
    "hwcdm = 0.6934669332795699\n",
    "\n",
    "np.savez('cosmology_parameters_21.npz',\n",
    "         chi2_lcdm=chi2_lcdm, Mlcdm=Mlcdm, omlcdm=omlcdm, wolcdm=wolcdm, walcdm=walcdm, hlcdm=hlcdm,\n",
    "         chi2_wwcdm=chi2_wwcdm, Mwwcdm=Mwwcdm, omwwcdm=omwwcdm, wowwcdm=wowwcdm, wawwcdm=wawwcdm, hwwcdm=hwwcdm,\n",
    "         chi2_wcdm=chi2_wcdm, Mwcdm=Mwcdm, omwcdm=omwcdm, wowcdm=wowcdm, wawcdm=wawcdm, hwcdm=hwcdm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3ca4587a-1240-4882-9bad-dc44359eaabf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Δχ^2 για 1σ, 2 παράμετροι: 2.295748928898636\n"
     ]
    }
   ],
   "source": [
    "#dchi\n",
    "import scipy.stats as stats\n",
    "import scipy.special as special\n",
    "\n",
    "def dchi(nsig, M):\n",
    "    p = special.erf(nsig / np.sqrt(2))\n",
    "    return stats.chi2.ppf(p, M)\n",
    "\n",
    "print(\"Δχ^2 για 1σ, 2 παράμετροι:\", dchi(1, 2)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7d7744c7-1d68-4bae-8fff-f076cec3b948",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Contours with Riess point\n",
    "#1)Contour om - Μ\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from joblib import Parallel, delayed \n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "om_vals1 = np.linspace(0.29, 0.34, 150) \n",
    "h_vals1 = np.linspace(-19.34, -19.45, 150)\n",
    "OM_grid1, M_grid1 = np.meshgrid(om_vals1, h_vals1)\n",
    "\n",
    "params1 = [(om, M) for om, M in zip(OM_grid1.ravel(), M_grid1.ravel())]\n",
    "results1 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total5)(M, om, wowcdm, wawcdm, hwcdm) for om, M in params1)\n",
    "Z_1 = np.array(results1).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_wcdm \n",
    "levels1 = [\n",
    "    chi2_min + dchi(1,2),  # 1 sigma \n",
    "    chi2_min + dchi(2,2),  # 2 sigma \n",
    "    chi2_min + dchi(3,2)  # 3 sigma \n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour1 = ax.contour(OM_grid1, M_grid1, Z_1, levels=levels1, colors='red', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(omwcdm, Mwcdm, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r'$M$')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9b7d1018-5f7c-463b-afe6-a56bb8bf7c8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2)Contour om - h\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from joblib import Parallel, delayed \n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "om_vals2 = np.linspace(0.27, 0.34, 150) \n",
    "h_vals2 = np.linspace(0.65, 0.72, 150)\n",
    "OM_grid2, h_grid2 = np.meshgrid(om_vals2, h_vals2)\n",
    "\n",
    "params2 = [(om, h) for om, h in zip(OM_grid2.ravel(), h_grid2.ravel())]\n",
    "results2 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total5)(Mwcdm, om, wowcdm, wawcdm, h) for om, h in params2)\n",
    "Z_2 = np.array(results2).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_wcdm \n",
    "levels2 = [\n",
    "    chi2_min + dchi(1,2),  # 1 sigma \n",
    "    chi2_min + dchi(2,2),  # 2 sigma \n",
    "    chi2_min + dchi(3,2)  # 3 sigma \n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour2 = ax.contour(OM_grid2, h_grid2, Z_2, levels=levels2, colors='red', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(omwcdm, hwcdm, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r'$h$')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3c3fbc8c-9950-4f6c-902a-97a30e4642fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3)Contour M - h\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from joblib import Parallel, delayed \n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "M_vals3 = np.linspace(-19.3, -19.45, 150) \n",
    "h_vals3 = np.linspace(0.65, 0.72, 150)\n",
    "M_grid3, h_grid3 = np.meshgrid(M_vals3, h_vals3)\n",
    "\n",
    "params3 = [(M, h) for M, h in zip(M_grid3.ravel(), h_grid3.ravel())]\n",
    "results3 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total5)(M, omwcdm, wowcdm, wawcdm, h) for M, h in params3)\n",
    "Z_3 = np.array(results3).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_wcdm \n",
    "levels3 = [\n",
    "    chi2_min + dchi(1,2),  # 1 sigma \n",
    "    chi2_min + dchi(2,2),  # 2 sigma \n",
    "    chi2_min + dchi(3,2)  # 3 sigma \n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour3 = ax.contour(M_grid3, h_grid3, Z_3, levels=levels3, colors='red', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(Mwcdm, hwcdm, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$M$') \n",
    "ax.set_ylabel(r'$h$')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dce202e3-1d24-4302-bab9-0edca37f8661",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#4)Contour om - w0\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from joblib import Parallel, delayed \n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "om_vals4 = np.linspace(0.27, 0.34, 150) \n",
    "wo_vals4 = np.linspace(-1.30, -0.95, 150)\n",
    "OM_grid4, wo_grid4 = np.meshgrid(om_vals4, wo_vals4)\n",
    "\n",
    "params4 = [(om, wo) for om, wo in zip(OM_grid4.ravel(), wo_grid4.ravel())]\n",
    "results4 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total5)(Mwcdm, om, wo, wawcdm, hwcdm) for om, wo in params4)\n",
    "Z_4 = np.array(results4).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_wcdm \n",
    "levels4 = [\n",
    "    chi2_min + dchi(1,2),  # 1 sigma \n",
    "    chi2_min + dchi(2,2),  # 2 sigma \n",
    "    chi2_min + dchi(3,2)  # 3 sigma \n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour4 = ax.contour(OM_grid4, wo_grid4, Z_4, levels=levels4, colors='red', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(omwcdm, wowcdm, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r'$w_{0}$')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "10af58ba-68fb-4dd3-9311-50a33434d497",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#5)Contour Μ - w0\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from joblib import Parallel, delayed \n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "M_vals5 = np.linspace(-19.1, -19.45, 150) \n",
    "wo_vals5 = np.linspace(-1.10, -0.95, 150)\n",
    "M_grid5, wo_grid5 = np.meshgrid(Μ_vals5, wo_vals5)\n",
    "\n",
    "params5 = [(M, wo) for M, wo in zip(M_grid5.ravel(), wo_grid5.ravel())]\n",
    "results5 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total5)(M, omwcdm, wo, wawcdm, hwcdm) for M, wo in params5)\n",
    "Z_5 = np.array(results5).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_wcdm \n",
    "levels5 = [\n",
    "    chi2_min + dchi(1,2),  # 1 sigma \n",
    "    chi2_min + dchi(2,2),  # 2 sigma \n",
    "    chi2_min + dchi(3,2)  # 3 sigma \n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour5 = ax.contour(M_grid5, wo_grid5, Z_5, levels=levels5, colors='red', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(Mwcdm, wowcdm, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$M$') \n",
    "ax.set_ylabel(r'$w_{0}$')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "65a4e1c1-a82a-4634-b570-dd6fd6619765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#6)Contour h - w0\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from joblib import Parallel, delayed \n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "h_vals6 = np.linspace(0.65, 0.72, 150) \n",
    "wo_vals6 = np.linspace(-1.40, -0.95, 150)\n",
    "h_grid6, wo_grid6 = np.meshgrid(h_vals6, wo_vals6)\n",
    "\n",
    "params6 = [(h, wo) for h, wo in zip(h_grid6.ravel(), wo_grid6.ravel())]\n",
    "results6 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total5)(Mwcdm, omwcdm, wo, wawcdm, h) for h, wo in params6)\n",
    "Z_6 = np.array(results6).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_wcdm \n",
    "levels6 = [\n",
    "    chi2_min + dchi(1,2),  # 1 sigma \n",
    "    chi2_min + dchi(2,2),  # 2 sigma \n",
    "    chi2_min + dchi(3,2)  # 3 sigma \n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour6 = ax.contour(h_grid6, wo_grid6, Z_6, levels=levels6, colors='red', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(hwcdm, wowcdm, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$h$') \n",
    "ax.set_ylabel(r'$w_{0}$')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "72c5a623-8964-4410-b223-68b07615586f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tAPCoToRMp9NZtlPP1q1b5XQ63ZbNyMjQvHnz1K1bNx05ckQffvihpkyZYlk2PT1dc+bMUVJSkhYuXGhbfzMyMnTo0CFJ0ldffWVbvQAuTd99J0VHS3asTHbrrez+A+DiqPUh88knn1R0dLQ+/fRTSdInn3yi6OhoTZ06tVy5goICNWvWTPHx8VqxYoUmTZqkgwcPatiwYW7rbtKkiUaMGKGYmBjdcccdtvS3f//+at26tU6dOiVJmjp1qpo3b64P+VsBqLO++EK66SZ76rrxRmnLFnvqAgBPHAavN9dK+fn5ioyMlCTl5eUpIiLCzz0CUFV33CH9+tfSXXdVv67Tp823zE+d4rlMoDYLhBxQ60cyAeBS5nRKGzdKAwfaU99ll0kNGkjHjtlTHwC4Q8gEgAC2e7f5LKadezskJJjbUwJATSJkAkAA++ILc9khOxEyAVwMhEwACGDr15sv69ipVy9CJoCaR8gEgADldEqbNkk33GBvvb16Sdu321snAFREyASAAJWcbL6oY+fzmJLUrJkUHCydOGFvvQBwIUImAASonTulq6+umbqvvlratq1m6gYAiZAJAAFr926pe/eaqbtnTzPEAkBNIWQCQIDavVvq2rVm6u7Rg5AJoGYRMgEgQO3ZI3XrVjN19+wp7dhRM3UDgETIBICAlJcnZWZK7drVTP1xceaLPwUFNVM/ABAyASAAffed1Llzze0vHhRk1r93b83UDwCETAAIQHv21NzzmKW6d5d27arZNgDUXYRMAAhAe/dKXbrUbBvdupkvFwFATSBkAkAA+u67mg+ZXbsyXQ6g5hAyASAAXYyRzK5dzWl5AKgJhEwACDC5ueanZcuabadZM+n8eSk7u2bbAVA3ETIBIMDs2+f7m+V795rLHvmqY0fpyBHfrwOAyhAyASDAlC5f5K3//le64QbpiiukuXOlwkLvr+3QQTp82OcuAkClCJkAEGBKRzK9cfy4dNdd0vLl0tq10hdfSFddJa1b5931HTsSMgHUjHr+7gAAoLx9+8yRycqcOyfdfrs0caL0s5+Zx1aulD79VPrtb6VDh6TgYM91dOggHTxY7S4DgAtGMgEgwOzfL115pecyhiH97nfmqOUf/lD+3ODBUvv20urVlbfVvr30ww9V7ysAuEPIBIAAcv68lJpqjjB68umn0s6d0sKF1i8IPfKINH9+5e116MCLPwBqBiETAALIoUNSbGzl09wrVkhjx0phYdbnb7vN3M3n++8919OunXT0aJW6CgAeETIBIIAkJ0udOnkuU1ws/ec/5vOY7tSvL40eLf3jH57ratRIKimp2vJHAOAJIRMAAsj+/ZWHzI0bzeWKKlus/be/lZYskYqKPJdr1046dsynbgJApQiZABBAUlKkyy/3XGblSumOOyqvq2NHqVu3yl8AatuWkAnAfoRMAAggP/xgvvHtjtMpvfee56nyCz34oLR4secybdvyXCYA+xEyASCAHD1qhj53vv1WatrUHKX0xm23SVu2SOnp7sswXQ6gJhAyASBAGIb0449Smzbuy7z3nhkcvRUWJl17rbn1pDtt2pg7BwGAnQiZABAgMjLMt71DQ92Xef99aeRI3+rt00fautX9+TZtzHALAHYiZAJAgDh2zPNUeUqKlJMj9ezpW73XXFP5SCYhE4DdCJkAECAqmyr/z3+kW2+13uHHk2uuMZ/lNAzr861bM10OwH6ETAAIED/+6Hkkc+1aaehQ3+tt2lQKD3f/ck9EhBlACwp8rxsA3CFkAkCAqGwkc/t2c1SyKhISzNFMd1q1MvdMBwC7EDIBIED8+KM5dW0lLc1cI7N586rVnZAgbdvm/nyrVkyZA7AXIRMAAoSnkcxdu6QePape99VXmyOh7rRqJZ04UfX6AaAiQiYABIjjx92PZO7cKXXvXvW6KwuZLVsyXQ7AXoRMAAgQqanmiKKVXbuqFzJbtJCKi81pdyuETAB2I2QCQADIzZXq1zffAreyZ4/UrVvV63c4zOn2Xbusz7dsKZ06VfX6AaAiQiYABIDjx92PYpaUSAcOSFdeWb02und3HzJbtJBOnqxe/QBwIUImAASA1FRzNNFKSor5QlBYWPXa6NHDfLbTCiETgN0ImQAQADw9j7lnj9S1a/Xb6NZN2rvX+lzz5kyXA7AXIRMAAsCJE+5D5t69Upcu1W/jyiul5GRz+r2iqCgpL898OQgA7EDIBIAAcOKE++nyPXvsCZlhYeYSSd9/73rO4TC3nzx9uvrtAIBEyASAgOApZH73nT0hUzLr+e4763PNmhEyAdiHkAkAAeDkSeuQWVwsHT4sxcfb085VV/FcJoCLg5AJAAHgxAnzDe+Kvv9eattWCgmxp53LLzffVrfSrJn7xdoBwFeETAAIACdPWofMffukzp3ta6dtW3OPdCuXXcZ0OQD7EDIBwM/OnTPf+I6IcD23f7+9IbN1a/chMzpaOnPGvrYA1G2ETADws1OnzOchrezfX/2dfi7UurW5u5CV6GgpM9O+tgDUbYRMAPCzykJmp072tRURYS5XlJfneo6RTAB2ImQCgJ95CpkHDtgbMiVzNDM11fV448ZSdra9bQGouwiZAOBnaWnWITM9Xapf39yNx07upswbN5aysuxtC0DdRcgEAD9LSzOXD6rohx+kDh3sb69VK/cjmYRMAHYhZAKAn7kLmcePm6OOdmvRwlyXs6KoKKbLAdiHkAkAfpaWZq5RWVFqqnch0+mUHnhA+vhj79pr2dI6ZDZqRMgEYB9CJgD4mbuQefy4ObVdmWXLzE9iorRnT+Xl3YXM8HBzzU6ns/I6AKAyhEwA8LP0dOuQeeJE5SGzoED6n/+RkpKk+fOlkSMr37XH3R7lDofUsKGUn+993wHAnXr+7gAA1HWnT7ufLm/Z0vO1L70k3Xij1Lu3+fnuO+nOO6V169zvd+4uZErmlHlOjhk2AaA6GMkEAD8yDHOXnSZNXM+dOOE5ZJ48Kc2dK82Y8X/Hpk0zr/nTn9xf16KFea0VnssEYBdCJgD4UX6+FBYm1bOYV6osZE6fLv32t1KbNv93zOEwg+Z//uP+ukaNzHaLi13PNWwo5eZ6338AcIfpcgDwo6wsc33KikpKzBHFmBjr6w4flt57T0pOdj131VXmS0PZ2dYLuTscUtOmUkaG6yLwhEwAdmEkEwD8KCvLOgiePm0GQYfD+rrPP5eGDTNHJSsKCjKfz/z6a/ftunsuk5AJwC6ETADwo6wsKTra9fjJk+azk+5s2yYlJLg/36ePtHWr+/NNm5pvtVdEyARgF0ImAPiRu5HMU6es9zMvtX271KuX+/O9e0vffOP+/GWXWYfMyEgpL8/9dQDgLUImAPiRu+cmPYXMkhJz0fXu3d3X27t35SOZVutpRkYykgnAHoRMAPCj7GzrF3/S0tyHzIMHzTfKGzRwX2+bNubuPe4WZm/SxHzxp6LISBZjB2APQiYA+FF2tvXLO2lpUrNm1tfs3Cn16OG5XodDuvpqaccO6/PuQmZEBCETgD0ImQDgRzk51tPl1Q2Zkhkyt2+3PudpJJNnMgHYgZAJAH7kbgvHtDTrrSYladcuz89jlurRwyxrJSaGkUwANYuQCQB+5G4kMz3dfcjcs0fq1q3yurt1M8taiYmRzpxxPd6ggVRQUHndAFAZQiYA+FFOjvUzmadPW4fMnBzzOc4Lt5J0p1MnKSXFevvI6Ghzz/SKIiIImQDsQcgEAD9yN12enm4+N1nRd9+Z20a62wnoQiEhUrt20qFDrueio92PZDJdDsAOhEwA8KPcXNeQWVho/m9YmGv5776TunTxvv7OnaV9+1yPN2pktm0Y5Y+Hh0tnz3pfPwC4Q8gEAD+yCpkZGdajmJIZMjt39r5+dyEzKMg6UPJMJgC7EDIBwI/y8nwLmfv2+R4y9++3PhcVZW5reSFGMgHYpc6EzE2bNmnYsGGaPn26x3LFxcV66aWX1KNHD3Xo0EEtW7bUmDFjlJqaWu0+5ObmKjY2VgMHDrQ8X1RUpJkzZ+rKK69UaGiomjdvrnvvvVcHDhyodtsAApPVSGZmpvuQuX+/byHz8svNHYKsREWZz4ReKDzc3CkIAKqr1ofMtWvXavDgwRowYIDWrFkjo+IDSBcoLCzU8OHDtXTpUq1cuVJHjhxRUlKStm3bpt69e1c77E2YMEGHDx+2PFdSUqKRI0fq6aef1sGDB1VcXKy0tDQtW7ZMvXv31ubNm6vVNoDAYxjm85ehoeWPZ2aaSwxVdO6cuX5m27bet1FZyMzOLn8sLIyRTAD2qPUhMy4uTmvWrNGgQYMqLTt16lStW7dOS5cuVVxcnCQpNjZWq1atUnp6ukaNGqViq7VAvLB69WqtWLHC7flXX31V+/fv13vvvae8vDwVFBRo8eLFioiIUG5urn7961/r/PnzVWobQGA6e9YcOaz4pnhmpvn2d0WHDkmxsebzlN5q3Nis3+pN8kaNrEMmI5kA7FDrQ2ZsbKyCg4N1zTXXeCyXnZ2t+fPnq127duratWu5cx06dNDIkSO1Y8cOLV++3Oc+ZGRkaPz48ZoxY4bbMv/+97+1fv16jRw5UuHh4QoNDdUDDzygV155RZL0ww8/aNOmTT63DSBw5eWZ2zhW5G4k89Ah6YorfG+nVSvp1CnX4w0bmtP1FwoKMkdYPUz6AIBXan3ILBVmtRbIBTZv3qyzZ8+qVatWludvueUWSapSyBw3bpzGjx/vEl5LFRUV6Ze//KXatWvncu7BBx9UzE9/25w+fdrntgEErvx8c/Hzis6csR7JPHhQio/3vZ2mTc11NyuyCpmSOX1fuowSAFRVnQmZjkpWLs78aeuL7IpzRz9p3769JGnr1q0+tbts2TKlpaVpwoQJbsvUr19fv/vd79yej//pb5XSPgCoHfLyfAuZhw6Zz1j6qkkTcwehiho2dH3xRzJDJlPmAKqrzoTMyrRu3VqSlJycrByLn7qlLwylWw0HuJGamqqnn35ar7/+uoJ8eYiqgvT0dLVs2VJ9+vSpch0AAk9BgXXIzMoyn6Ws6OhRcwcfXzVpYv1MZmSk9e4+jGQCsAMh8yd9+/ZVixYtVFxcrEWLFrmcP378uCQpJCTE6zrHjBmj6dOnV2sE8vDhw/r+++/1+OOPVxpUCwsLlZOTU/YBENjcTZdnZ1uHzPR06/3MK+Nun/LISHM0tSJCJgA7BHzIfOqpp+RwOHz6jB492ud2QkNDtXjxYoWEhGjatGlasmSJioqKlJubq7feekuzZ8+W5P2U9cKFCxUeHq7ExESf+3KhuXPn6qqrrtJjjz1WadmZM2cqKipKUVFRbp8tBRA4PI1kRkW5Hj992ny+0lcxMeYC7xURMgHUpIAPmbNmzZJhGD59lixZUqW2hg4dqs2bN2vo0KGaNm2arrvuOo0dO1aNGjUqmy7v27dvpfWkpKTohRde0KuvvlqlfpQ6cOCAXnvtNS1btsyrEdQpU6YoOztb2dnZtiweD6BmFRSY2zhW5GkksyohMzradWcfyQy4VltIhoZKRUW+twMAF6rn7w4EmoSEBK1atarcse3btyslJUWSNGrUKI/XO51OJSYm6uWXX1bTqvxt8JPz589r9OjRWrRokXr06OHVNaGhoQr9aVXn4ODgKrcN4OLwFDIbNSp/rHRkseLC7d5o3Ng6ZLrbpzwkRGJZXgDVRcj0wosvvihJ6t69u4YMGeKxbFJSkpKSkjRixAi3ZTZs2FD2tru7HYgeffRR3X333frlL39ZxV4DCHRnz1qHzJwc15Dpbu1Mb/gaMuvXJ2QCqD5CZiW2bNmiZcuWKSgoSAsWLKh0KaTQ0FB16tTJ8lxBQYGOHTum8PBwyzUxSz333HO67LLLPC57BODSV1Bg7vhzIafTHLWsuLRvdUKm1faRkueQyXQ5gOoiZHqQnZ2txMREGYah559/Xv369av0mj59+mj//v2W59avX69BgwapT58+Wr9+vWWZuXPn6uTJk5o3b57Lufz8fOXn56tZs2Y+fQ8Agal0W8kLudsFyN3amd5o1Mh60fXwcOt9ygmZAOwQ8C/+2KV0z/GSkhKvymdmZmro0KE6dOiQZsyYoYkTJ1qWmzx5svr376+9e/dWu4/z5s3Trl27NHfuXJdz33//ve68804VWA07ALgkWY1k5uaai6RX5G4/c2+4W3Q9LIyQCaDm1ImRTKfTWbZTz9atW+V0Ot2uOZmRkaG33npLM2fOlGEY+vDDDzVs2DDLsunp6ZozZ44kc8kiq3DorVmzZmnKlCmKiYlxefHo3Llzys/P1/XXX68OHTpUuQ0AgeXcOddp8bw865CZlVW9kUxfQybPZAKorlo/kvnkk08qOjpan376qSTpk08+UXR0tKZOnVquXEFBgZo1a6b4+HitWLFCkyZN0sGDB90GTElq0qSJRowYoZiYGN1xxx1V7uOCBQs0ZcoUSeYIakZGRrlP/k9bctx7771VbgNA4Dl3znq63CpkulvWyBvupsXDwqzXw6xfX/Jy0gcA3Kr1I5nPP/+8nn/++UrLNWjQQGlpaT7V7XA49P7773tdfuDAgZZvk48bN07jxo3zqW0Alz53I5lWz2RmZ1sv0O6NoKD/G528cMlddyGzXj3ppyeMAKDKav1IJgAEqnPnXNe99LTVZFVDpmTWWfHln9BQsw8V1avHM5kAqo+QCQB+YrVUkbuQmZvrunamLxo0cJ0yDwmxDpPBwUyXA6g+QiYA+InVdHl+vvsF2q2e1fSW1ZqY7nb2qVePkAmg+giZAOAn586Vf0ZScr/VpLuljbxlFTKDg62fvWQkE4AdCJkA4Cfnz7s+k+kuZLp7IchbVi/5lG5gVvF9xOBgc+chAKgOQiYA+MnFDpnuXvKpOGoZFMRIJoDqI2QCgJ+cP28uLXQhq60mJTNkWr0Q5C13C69bTZkTMgHYgZAJAH5iNZJptUC7ZI5wVidk+vImeVCQ6xQ6APiKkAkAflJxcXTJeu1MyRyFtJpG95a7N8mtnr90OHgmE0D1ETIBwE+spsutgqfk/llNb4WEWO/uYzU1HhREyARQfYRMAPCToiLvQqZhWJf1Rf361ssVWQXK0rfOAaA6CJkA4CdWwdHqWHFx9QKm5H4/cofD+vlLnskEUF2ETADwE29DptXOQL7yJWQykgnADoRMAPCT4mIz/F3IXci0ehnIF+5292EkE0BNIWQCgJ9YhUyrqXGrpY585W5ZInchk9FMANVFyAQAPzEMM/xdqKjINXi6e+PcF+7eGCdMAqgp9SovAgCwm7vpaKvRTauljkqdO3dO77zzjt577z1lZGSoSZMmuu2223T33Xcr7IIHOT2tfVmxL0yVA7ADIRMA/MDpdB3FlMw1K4ODyx9zt3zRBx98oNGjR+vMmTMKCgqS0+lUUFCQVq5cqT/84Q96/fXXdeutt0pyP2JpGK7n3PUNAHzBjxEA8AOrMOnuuNXo5gcffKDbbrtNWVlZkiTnT8OUpf+blZWlkSNH6oMPPqi0LxVDplXwBABfETIBwA+qEzLPnTun0aNHS5IMN3PbpcdHjx6tc+fO/XTMqpz1MUImgOoiZAKAH7gLmU6n6/GKx9555x2dOXPGbcAsZRiGzpw5o3fffdftFLg37QFAVRAyAcAP3AU5qzBYsex7772nIC8fmgwKCtKqVass32T31B7PZAKoLn6MAIAfOJ3WU9JWxysey8jIKHv2svJ2nMrMzKxWewBQFYRMAPADd6OFVs9DVhyFbNKkiU8jmTExMSopcX15qLQfFUdU3U3lA4AvCJkA4Afupq+tjld89PK2227zaSTz9ttvV3Gx+xeNKrZHyARgB0ImAPiBuylpb97svvvuuxUdHS1HJQUdDoeio6N11113eXzRiGcyAdQEfowAgJ/48tzjhaOZYWFhev3113+qw7qS0uOvv/66wsLCVFRkvTWlu/3TrabWAcAXXoXM3r17a+zYsXr11Vf1zTff6Pz58zXdLwCo1XzZutHhcC1/66236r333lPjxo0lqewZzdL/bdy4sd5///2yHX/cbU1pNY1OyARgB69+jGzbtk3bt2/XP//5T/OievXUpUsX9erVSwkJCUpISFCPHj0UGhpao50FgNrE25HMoCDrfcdHjBih1NRUvfvuu1q1apUyMzMVExOj22+/XXfddVe5vcuttqYsnZqv2A+eyQRgB59/VzUMQ0VFRdqxY4d27typ1157TZIUHByszp07KyEhoSx89uzZU+Hh4bZ3GgBqs4qjlu5CpmROnd9333267777PNZZWOg6Xe7ujXN3e6UDgC+8Cpmlz/a0adNGDzzwgNq1a6dvv/1W27Zt065du3T27FkVFxdr9+7d2rNnT9mzQkFBQerUqVPZaOdjjz1Wc98EAGoBq6nx4GAzEFZHYaFUcbLp/HnXY5I5XU7IBFBdXj2T+e233+r666/XsWPH9Oyzz+rtt9/WI488oi1btig3N1e7du3Sa6+9pkcffVT9+vVTgwYNZBiGSkpK9N1332np0qV6/PHHa/q7AMAlz2oK3Y6QaRUoz5/3/mUgAPCVVz9GevbsqY0bN2r58uWaNGmS1q5dq549e+rhhx/W9OnT1bVrV3Xt2lWJiYmSzCn15OTkstHOb775Rjt37qzRLwIAlxqrl38cDtep8fr1zSns6jh3TrrgEU1J1lPoktkWIRNAdfm0hNGvfvUrJScna+rUqapXr57mzp2ryy+/XIsWLZJxwU9Lh8OhK6+8Uvfee69efPFFbdiwQVlZWXb3HQAuWVbT4pI5alkTIfPsWalBg/LHrIKn5H6EEwB84fM6meHh4frzn/+s/fv36/bbb1d6eroefvhh9erVSxs3bqyJPgJAreMuZFq95GNXyKz4Hua5c9bPZPLiDwA7VHkx9nbt2undd9/VZ599pi5dumjnzp0aNGiQRo0apaNHj9rZRwCoddy9MR4U5Pr8ZUiIObpYHQUF1iGTkUwANaXaO/4MGjRIO3bs0Ny5c9W4cWO988476ty5s5555hmdPXvWjj4CQK0TFOR+urxiyAwNNZ+frI6CAikiovwxq9FNyf1b5wDgC1u2lQwKCtIjjzyiQ4cO6eGHH9a5c+f07LPPavr06XZUDwC1jruRzJoKmfn51iGTkUwANaVa7w/m5uZqz5492r17d9lnz549MgxDDodDTnerBwNAHWc1LS6Zb3UXF5c/FhZmTm1XldNpjppW3MXH3Uimu7fOAcAXXoXM4uJi7d+/v1yY3L17t44dO1ZW5sK3y9u0aaOePXvqxhtvtL/HAFALuBvJrFfPNXzWr2+OLpZuA+mrvDzXUUzJDJlWx60WbgcAX3kVMiMiIlT806/WF4bJevXqqVOnTurZs6euvvpq9ezZUz179lRMTEzN9BYAagl3C6xbjWQ6HObIYlFR1UYY8/Kkhg1djxcUuC5rJBEyAdjDq5BZVFQkh8Ohjh07atiwYerRo4d69uyprl27KpSfRADgM08h02q5ovBwMxRWJWTm5kqRka7HCZkAapJPz2QePnxY77zzjlJSUnTkyBEdPXpUvXr1Uvv27WuqfwBQK7kLme7WxIyIMENh48a+t5WTIzVq5Hrc6mUgyZyaZ51MANXlVchs06aNfvzxR0nSqVOntGbNGn388cdl56Ojo3X11VerV69eZZ/LL7+8ZnoMALWAu2cr3a2JGRlphsKqyMmRoqJcj1s9q1m6b3lVnv0EgAt5FTKPHj2qzMxMffvtt2X7kX/77bc6fPiwJCkzM1OfffaZPv/887JrIiMj1bNnz3LBs0uXLjXzLQDgElS6T3nQBYvJeRrJzMurWjvZ2e5HMlu0KH/M3S5AAOArr6fLY2Ji9LOf/Uw/+9nPyo5lZWVp27ZtZaFz27ZtOnTokAzDUG5urr788ktt2rRJkrmfeXHFp9kBoA4rfZP8wpAZGmodMhs2rF7ItJpmz82V4uPLHzt3znpZIwDwVbXWyWzcuLFuuukm3XTTTWXHcnNztX379nLBMzk5udxb6QCA/3vJ58LnH0NDrdfEbNjQDIVVceaMdci0euvc3dqZAOCraoVMKw0bNtQNN9ygG264oexYQUGBduzYYXdTAHBJs5oaDwszg15FDRuaz1ZWRVaW9TOZVm+dEzIB2MWWbSUr06BBA/Xr1+9iNAUAl4z69V3XxAwPtx7JjIqqesg8c0ayWr44N9f1WU1CJgC7XJSQCQBwVbqTz4UaNDCXKqooKsockayKzEwpOtr1uNXSRu7WzgQAXxEyAcBPrEJmeLj1UkVRUeYLPFXhbiTTKmQykgnALoRMAPCT0q0iL+RuJLNx46qPZGZkWIdMq6WNGMkEYBdCJgD4SWio60hmRIT1SGZMjDkiWRXp6dJll7kez852fSHI3S5AAOArQiYA+ElIiLlP+IXchczoaPPZSl85ndLJk66LrhcVmYvBV9w+kpFMAHYhZAKAn1iFzMhI60XXY2KqFjJPnpSaNHENk1ajmJIZcAmZAOxAyAQAPwkLcw2ZDRtaj2Q2aWI+W+mr77+X4uJcj2dlWb9xnp/vunYmAFQFIRMA/CQ01Hok02pnnyZNzGcrffX991JsrOtxT7sA8UwmADsQMgHAT8LCXBded7d9ZIMG5sLtFV8UqkxKinXIzMqyDpm8+APALoRMAPATq+nyRo3c71HetKnvo5kHD0qXX+56PDPTelkjq/3MAaAqCJkA4CdW+5S7my6XpGbNpNOnfWvj4EEpPt71uKeQyTOZAOxAyAQAP7EKmcHBUr16riOckhkyT570vn7DkA4dch8yrV78IWQCsAshEwD8JDzc9ZlMyf0WknFxZmj0Vlqa2UbFXX0kM2Q2aeJ6PDeX6XIA9iBkAoCfhIe7jmRK5gs5ViGzc2dp3z7v609Olq680vpcRoZ1yOSZTAB2IWQCgJ+4C5lRUdb7lHfuLH33nff1JydLnTpZn3O3nzkjmQDsQsgEAD9xFzKjo61DZteu0p493te/b58ZTK1kZFjvZ56TQ8gEYA9CJgD4SYMG5l7hFTVubC6WXlHTpuaLQadOeVf/d99JV11lfe70abO+ihjJBGAXQiYA+Im7kBkdbR0yJalbN2nXLu/q9zSSmZ7u+kxmSYlUVGTuRAQA1UXIBAA/cRcyY2LMt7+t9Owp7dxZed05OeaoZKtWrueKisydgyru7JOba/0mOgBUBSETAPwkIsLcxrEiTyHz6qul7dsrr3vvXqlLF8nhcD2Xnm5OlVc8l5NDyARgH0ImAPiJu5DZpIn5Yo6Vnj29C5m7d5svClk5fdr9Sz9RUZXXDQDeIGQCgJ9ERroPme5GMq+4QkpNNdez9GTXLvP5TStpadYhMzubkUwA9iFkAoCfRERYh8WmTc0pbSvBwVKPHtK2bZ7r3rXLLGfl1CmpeXPX41lZ5pvtAGAHQiYA+Im7kUxPIVOS+vSRvv7a/XnDMKfLPY1kWoXM7GymywHYh5AJAH4SEWG+0V1R06ZmEHSnTx/pv/91f/6HH8xlkNxNfZ86JTVr5no8K4uQCcA+hEwA8BN3I5mRkdK5c+ZSQ1b69pW2bHFf75dfSgkJ7s+fPCm1bOl6PCvLDKcAYAdCJgD4SVCQVK+euWblhRwOczTT3RvmbdqYZY4dcz1nGNKLL0pjx7pv99QpqUUL1+NnzhAyAdinzoTMTZs2adiwYZo+fbrHcsXFxXrppZfUo0cPdejQQS1bttSYMWOUmppa7T7k5uYqNjZWAwcOdFvmzTffVK9evRQREaGWLVvq4YcfVpbVJsYAaoXISOsp82bNPE+Z9+snbd7senz1aikkRLr5ZvfXnjhh/UxmRobrLkAAUFW1PmSuXbtWgwcP1oABA7RmzRoZhuG2bGFhoYYPH66lS5dq5cqVOnLkiJKSkrRt2zb17t1bBw4cqFZfJkyYoMOHD7s9P336dP3mN7/R6dOn5XQ6dfLkSS1YsEDDhw+X0+msVtsAAlOjRu5Dpqc9ygcMkDZuLH/MMKQ//1n64x+tF2EvdeKE9XS5u/UzAaAqan3IjIuL05o1azRo0KBKy06dOlXr1q3T0qVLFRcXJ0mKjY3VqlWrlJ6erlGjRqm4uLhK/Vi9erVWrFjh9vxXX32l9evX69ChQzp27JhycnI0depUSdLmzZv14YcfVqldAIGtUSNzEfSKmjf3HDKHD5dWrpQ2bPi/Yx9/bO4/Pny4++uKi832rKbFS3cCAgA71PqQGRsbq+DgYF1zzTUey2VnZ2v+/Plq166dulbYJqNDhw4aOXKkduzYoeXLl/vch4yMDI0fP14zZsxwW+brr7/WRx99pNjYWElS/fr19eyzz2rAgAGSpOTkZJ/bBRD4GjasWsjs2FF6911p1Chp7VpzFHP69MpHMUvfLLcqw0gmADvV+pBZKiwszOP5zZs36+zZs2rVqpXl+VtuuUWSqhQyx40bp/Hjx7uE1ws99thjCg8Pdzl+3XXXSZJ69uzpc7sAAl9UlPV0eYsW5lvgnlx/vfTBB1JiovSHP5ijlCNGeL7mxAmpdWvrc+52AgKAqqgzIdPh6Vd7SZk/7eGWnZ1teb59+/aSpK1bt/rU7rJly5SWlqYJEyb4dF2pkydP6vrrry8LuQBql0aNzEXQK/ImZErmmpmffiq9/740Y4bnUUzJ3JLS6nfpwkJzNNTid10AqJJ6/u5AoGj906/2ycnJysnJUaMKqxiXvjCU7mkbjgpSU1P19NNPa8OGDQoK8j3PHzt2TLt379aHH35YaUgGcGmKirIOmS1bmqOO3ujaVTpypPKAKUk//mgdMpkqB2C3OjOSWZm+ffuqRYsWKi4u1qJFi1zOHz9+XJIUEhLidZ1jxozR9OnTy0ZBffH111/r5ptvVsuWLVVQUODVNYWFhcrJySn7AAh8UVHWz2T6EjIl7wKmJKWkSD+911iOu60mAaCqAj5kPvXUU3I4HD59Ro8e7XM7oaGhWrx4sUJCQjRt2jQtWbJERUVFys3N1VtvvaXZs2dLkteBceHChQoPD1diYqJP/fjxxx81duxY3XjjjTp48KDWrFmjXr166csvv6z02pkzZyoqKkpRUVFuny0FEFiiosyddipq2dKc2rbbgQPSFVe4Hk9Ls95qEgCqKuBD5qxZs2QYhk+fJUuWVKmtoUOHavPmzRo6dKimTZum6667TmPHjlWjRo3Kpsv79u1baT0pKSl64YUX9Oqrr/rchzZt2mjRokVKT0/X/Pnz1bhxY+Xl5en+++9Xkbs95n4yZcoUZWdnKzs725bF4wHUPHchs2FDczkiLycyvHbwoHT55a7H3e1nDgBVxTOZFSQkJGjVqlXljm3fvl0pKSmSpFGjRnm83ul0KjExUS+//LKaVmPBuYiICD388MPq16+frrnmGh05ckRbtmzRDTfc4Paa0NBQhYaGSpKCg4Or3DaAi6dxY+tnMiXzLfDUVCk+3p62iovNZzI7dHA9d+oU0+UA7BXwI5mB4MUXX5Qkde/eXUOGDPFYNikpSUlJSRoxYoTLNH7pgvAbNmwoO1aZnj176o477pAkRieBWig62nokUzJD5k+Pg9vi8GGpbVtzv/SKCJkA7MZIZiW2bNmiZcuWKSgoSAsWLKg0GIaGhqpTp06W5woKCnTs2DGFh4erXbt2Xvehf//+WrFihVpa7QMH4JLWuLF05oz1uTZtzJFHuyQnS25+POnkSenaa+1rCwAImR5kZ2crMTFRhmHo+eefV79+/Sq9pk+fPtq/f7/lufXr12vQoEHq06eP1q9f71M/mjVrVrYwO4Dao3Fj9yOZdofM/fvdh0xGMgHYrc5Ml5fuOV5SUuJV+czMTA0dOlSHDh3SjBkzNHHiRMtykydPVv/+/bV3717b+nqh8+fP6+2339asWbPKnrcEUHtER0s/7QXhok0b6dgx+9rav1/q3Nn63IkT5hvtAGCXOhEynU5n2U49W7duldPpdFs2IyND8+bNU7du3XTkyBF9+OGHmjJlimXZ9PR0zZkzR0lJSVq4cGGV+1dSUqKrr75a8fHx+utf/6q8vDxJ0qlTp/SrX/1KiYmJeuCBB6pcP4DAVfpM5k8LWJTTtq29IXPfPunKK63PnTxJyARgr1ofMp988klFR0fr008/lSR98sknio6O1tSpU8uVKygoULNmzRQfH68VK1Zo0qRJOnjwoIYNG+a27iZNmmjEiBGKiYkpezmnKoKDgzVkyBDl5+dr4sSJatu2rW666SY9++yz+stf/qInnniiynUDCGzBwVJkpPUb5u3aSUeP2tOOYbgPmYWF5icy0p62AECSHIZh9fszLnX5+fmK/OlvjLy8PEVERPi5RwDc6dpVWrnSdZH0s2fN5ySzsqQq7ExbzsmTUkKC9dvqP/wg3XyzdOhQ9doAEDgCIQfU+pFMAAh0zZqZO+5UFB4utWhhLj1UXfv2SVddZX3uxAnr/cwBoDoImQDgZ82bm293W+nWTdq9u/pt7N3rOWTyPCYAuxEyAcDPPIXM7t2lXbuq38bevVKXLtbnUlMZyQRgP0ImAPhZ8+bmM5NW7AyZXbtanyNkAqgJhEwA8LMWLdyHzB49pJ07q1e/YXieLj9+nOlyAPYjZAKAn3kKmR06SOnpUm5u1etPTZUaNDB3F7Jy/Li58DsA2ImQCQB+1rKl+fKNlaAgc8q8OqOZu3ebLxC5c/y41Lp11esHACuETADwM08hU5J69pS2b696/ZWFTJ7JBFATCJkA4GeXXSZlZEjudrxNSJC+/bbq9e/aZT7baSU31xwtZb8GAHYjZAKAnwUFSU2bSqdPW5+vbsjcscOccrfy4488jwmgZhAyASAAtGxpveWjZO43fvSolJ/ve72nTpkvFXXubH3+xx95HhNAzSBkAkAAaN3afDbSSnCw+Uzljh2+17tmjTR4sFmHlWPHpLZtfa8XACpDyASAANC6tTmq6E6fPtLXX/te7+rV0vDh7s//+CMhE0DNIGQCQABo3dr9dLkkXXON7yGzqEj6/HPp5z93X4aRTAA1hZAJAAGgTRvPIfPaa30PmV9+ae7yExPjvgwhE0BNIWQCQABo08bzdHnHjuZyQ2lp3tf5n/9It97quczRo1K7dt7XCQDeImQCQABo29ZzyHQ4pOuvl774wrv6DMMMmSNGeC5z7BhLGAGoGYRMAAgArVubgc8w3Je57z7pn//0rr59+8xg2qmT+zInT5r7mYeH+9RVAPAKIRMAAkB4uPnJynJfZsQIMzwmJ1de33vvSSNHmkHTneRkzyEUAKqDkAkAAaJtW3M005369aXf/lZauLDyut5/3wyZniQnS1dc4VsfAcBbhEwACBDt2pkv4njy299Kb74p5eW5L5OaKh05IvXr57kuRjIB1CRCJgAEiHbtpB9+8FymVSvpllukN95wX+a556R77nG/y0+p/fvNLSsBoCYQMgEgQLRvX/lIpiT94Q/SK69ITqfruffekz77zAyalUlOJmQCqDmETAAIEO3bm9Pclbn2WvOt8I8/Ln/82DHp4Yelt96SIiM913H2rLnmJguxA6gphEwACBAdOlQ+XV5q0iTpkUekBQuknBypuNicIn/qKalXr8qvP3BAio+XgvhbAEAN4ccLAASIDh28G8mUpDvukJYulZKSpNhY6cYbpaZNpUcf9e76/fulzp2r2lMAqFw9f3cAAGCKiZEKCsypbG8WSO/f3/ykp0v//rf0y196XhfzQvv2ETIB1CxGMgEgQDgc3j+XeaGmTaWxY6XoaO+v+e476aqrfGsHAHxByASAABIbKx0+XPPtEDIB1DRCJgAEkPh486WcmlRUZI6WxsfXbDsA6jZCJgAEkC5dpD17araNQ4fMafn69Wu2HQB1GyETAAJI9+7S7t0128bevVLXrjXbBgAQMgEggFx1lfnmt9VuPnbZu9ccMQWAmkTIBIAA0qCB1LKllJJSc23s2UPIBFDzCJkAEGC6d5d27aq5+vfskbp1q7n6AUAiZAJAwOnRQ9q5s2bqPndO+vFHKS6uZuoHgFKETAAIMD17Sjt21Ezd+/dLV1whBQfXTP0AUIqQCQABpiZD5u7dTJUDuDgImQAQYFq3Nvcvz8iwv+4tW6Q+feyvFwAqImQCQIBxOKSrr5a2b7e/7o0bpQED7K8XACoiZAJAAOrVS9q2zd4609OlkydZvgjAxUHIBIAAVBMh88svpf79pSB+8gO4CPhRAwABqFcv6dtv7a1zwwbpxhvtrRMA3CFkAkAAioszX/zJyrKvzvXrpYED7asPADwhZAJAAHI47J0yz8yUjh0zdxMCgIuBkAkAAap3b2nrVnvq2rBBuv56FmEHcPEQMgEgQF1zjX0h84svpJtusqcuAPAGIRMAAlTfvtKmTZLTWf26vvhCGjSo+vUAgLcImQAQoFq1Mj9ff129ek6fNtfHZDtJABcTIRMAAtjtt0urVlWvji++MN8qZ31MABcTP3IAIICVhkzDqHodn33G85gALj5CJgAEsC5dzID53XdVu97plD79VLr5Znv7BQCVIWQCQABzOKQ775RWrqza9Z9/Ll12mXTFFfb2CwAqQ8gEgAB3113SO+9U7doFC6Rx4+ztDwB4g5AJAAEuIUHKyZGSk327LjVV2rhRGjWqZvoFAJ4QMgEgwDkcVRvN/Oc/pXvvlRo0qJl+AYAnhEwAuASMGiWtWOF9+aIi6dVXmSoH4D+ETAC4BPTuLeXlSXv2eFf+gw+kzp2lTp1qtl8A4A4hEwAuAQ6HNGGC+fFmzcz586WHH675fgGAO4RMALhEPPywVFAg/etfnsvt2CGlpEi33npRugUAlgiZAHCJCA6WFi+Wpk6VfvzRusyJE+a6mnPmSPXqXdz+AcCFCJkAcAnp1El6/HHpt791nTbPypJ+/nPpkUdYtgiA/xEyAeASM3GilJ0t/eUv0g8/mGGzoMCcHh82zAyhAOBvhEwAuMQEB0v/+7/SN99IgwZJ0dHmm+RXXinNmOHv3gGAiSd2AOASFBsrvfee+c+5udLhw1KXLuZb6AAQCAiZAHCJa9hQ6t7d370AgPKYLgcAAIDtCJkAAACwHSETAAAAtiNkAgAAwHaETAAAANiOkAkAAADbETIBAABgO0ImAAAAbEfIBAAAgO0ImQAAALBdnQmZmzZt0rBhwzR9+nSP5YqLi/XSSy+pR48e6tChg1q2bKkxY8YoNTW12n3Izc1VbGysBg4c6PU18+bNk8Ph0Pr166vdPgAAwMVS60Pm2rVrNXjwYA0YMEBr1qyRYRhuyxYWFmr48OFaunSpVq5cqSNHjigpKUnbtm1T7969deDAgWr1ZcKECTp8+LDX5Q8ePKjJkydXq00AAAB/qPUhMy4uTmvWrNGgQYMqLTt16lStW7dOS5cuVVxcnCQpNjZWq1atUnp6ukaNGqXi4uIq9WP16tVasWKF1+VLSkp0//33Kyio1t8iAABQC9X6BBMbG6vg4GBdc801HstlZ2dr/vz5ateunbp27VruXIcOHTRy5Ejt2LFDy5cv97kPGRkZGj9+vGbMmOH1NbNmzVJcXJwSEhJ8bg8AAMDfan3ILBUWFubx/ObNm3X27Fm1atXK8vwtt9wiSVUKmePGjdP48eNdwqs7O3fu1OLFizVv3jyf2wIAAAgEdSZkOhwOj+czMzMlmSOaVtq3by9J2rp1q0/tLlu2TGlpaZowYYJX5c+fP6/7779fixYtUuPGjX1qCwAAIFDU83cHAkXr1q0lScnJycrJyVGjRo3KnS99YSg9Pd3rOlNTU/X0009rw4YNXj9bOW3aNA0aNKhs5BQAAOBSVGdGMivTt29ftWjRQsXFxVq0aJHL+ePHj0uSQkJCvK5zzJgxmj59etkoaGWSkpK0evVqzZw50+s2LlRYWKicnJyyDwAAgL8EfMh86qmn5HA4fPqMHj3a53ZCQ0O1ePFihYSEaNq0aVqyZImKioqUm5urt956S7Nnz5YkrwPjwoULFR4ersTERK/K5+fn66GHHtLrr7+u8PBwn/svSTNnzlRUVJSioqLcPlsKAABwMQR8yJw1a5YMw/Dps2TJkiq1NXToUG3evFlDhw7VtGnTdN1112ns2LFq1KhR2XR53759K60nJSVFL7zwgl599VWv2544caJ+/etfV+tt8ilTpig7O1vZ2dm2LB4PAABQVTyTWUFCQoJWrVpV7tj27duVkpIiSRo1apTH651OpxITE/Xyyy+radOmXrX58ccfa+fOnZo/f37VOv2T0NBQhYaGSpKCg4OrVRcAAEB1EDK98OKLL0qSunfvriFDhngsm5SUpKSkJI0YMcJtmQ0bNpS97W4Yhp5//nl99dVXqlfP/e0oXUz+T3/6k5555hkfvwEAAMDFRcisxJYtW7Rs2TIFBQVpwYIFlS6FFBoaqk6dOlmeKygo0LFjxxQeHq527dqVHW/durXba44ePaqzZ8+qbdu2atCggdejowAAAP5EyPQgOztbiYmJZaON/fr1q/SaPn36aP/+/Zbn1q9fr0GDBqlPnz5av3592fE33njDbX0DBw7Uhg0b9MYbb2jgwIG+fgUAAAC/CPgXf+xSuud4SUmJV+UzMzM1dOhQHTp0SDNmzNDEiRMty02ePFn9+/fX3r17besrAADApa5OhEyn01m2U8/WrVvldDrdls3IyNC8efPUrVs3HTlyRB9++KGmTJliWTY9PV1z5sxRUlKSFi5cWCN9BwAAuBTV+pD55JNPKjo6Wp9++qkk6ZNPPlF0dLSmTp1arlxBQYGaNWum+Ph4rVixQpMmTdLBgwc1bNgwt3U3adJEI0aMUExMjO64444a/R4AAACXEodRugAkapX8/HxFRkZKkvLy8hQREeHnHgEAgIslEHJArR/JBAAAwMVHyAQAAIDtCJkAAACwHSETAAAAtiNkAgAAwHaETAAAANiOkAkAAADbETIBAABgO0ImAAAAbEfIBAAAgO0ImQAAALAdIRMAAAC2I2QCAADAdoRMAAAA2I6QCQAAANsRMgEAAGA7QiYAAABsR8gEAACA7QiZAAAAsB0hEwAAALYjZAIAAMB2hEwAAADYjpAJAAAA2xEyAQAAYDtCJgAAAGxHyAQAAIDtCJkAAACwHSETAAAAtiNkAgAAwHaETAAAANiOkAkAAADbETIBAABgO0ImAAAAbEfIBAAAgO0ImQAAALAdIRMAAAC2I2QCAADAdoRMAAAA2I6QCQAAANsRMgEAAGA7QiYAAABsR8gEAACA7QiZAAAAsB0hEwAAALYjZAIAAMB2hEwAAADYjpAJAAAA2xEyAQAAYDtCJgAAAGxHyAQAAIDtCJkAAACwHSETAAAAtiNkAgAAwHaETAAAANiOkAkAAADbETIBAABgO0ImAAAAbEfIBAAAgO0ImQAAALAdIRMAAAC2I2QCAADAdoRMAAAA2I6QCQAAANsRMgEAAGA7QiYAAABsR8gEAACA7QiZAAAAsB0hEwAAALYjZAIAAMB2hEwAAADYjpAJAAAA2xEyAQAAYDtCJgAAAGxHyAQAAIDtCJkAAACwHSETAAAAtiNkAgAAwHZ1JmRu2rRJw4YN0/Tp0z2WKy4u1ksvvaQePXqoQ4cOatmypcaMGaPU1NRq9yE3N1exsbEaOHCgx3Ivv/yyHA6Hy2fy5MnV7gMAAMDFUOtD5tq1azV48GANGDBAa9askWEYbssWFhZq+PDhWrp0qVauXKkjR44oKSlJ27ZtU+/evXXgwIFq9WXChAk6fPiwxzLnz5/XSy+95HK8fv36evTRR6vVPgAAwMVS60NmXFyc1qxZo0GDBlVadurUqVq3bp2WLl2quLg4SVJsbKxWrVql9PR0jRo1SsXFxVXqx+rVq7VixYpKy73xxhu6/vrrtW/fvnKf5ORktWnTpkptAwAAXGy1PmTGxsYqODhY11xzjcdy2dnZmj9/vtq1a6euXbuWO9ehQweNHDlSO3bs0PLly33uQ0ZGhsaPH68ZM2Z4LOd0OvXCCy/oj3/8o6688spyn44dO/rcLgAAgL/U+pBZKiwszOP5zZs36+zZs2rVqpXl+VtuuUWSqhQyx40bp/Hjx7uE14reeecdZWdn6+uvv9YPP/zgczsAAACBos6ETIfD4fF8ZmamJHNE00r79u0lSVu3bvWp3WXLliktLU0TJkyotOysWbN08uRJPfDAA+rYsaNuvvlmff311z61BwAAEAjq+bsDgaJ169aSpOTkZOXk5KhRo0blzpe+MJSenu51nampqXr66ae1YcMGBQV5zvP79u1TaGio4uLi9P3338swDH3++efq27ev5syZo4kTJ/r0fS58wSk/P9+nawEAwKXtwr/7Pb30XJMImT/p27evWrRooZMnT2rRokV68skny50/fvy4JCkkJMTrOseMGaPp06eXjYJ60rlzZ3311VeSpLS0NL355puaPn26srOz9cQTTyg6OloPPvigxzoKCwtVWFgoSTp9+nTZ8ebNm3vdZwAAULsUFBQoMjLyorcb8NPlTz31lOWakZ4+o0eP9rmd0NBQLV68WCEhIZo2bZqWLFmioqIi5ebm6q233tLs2bMlyavAKEkLFy5UeHi4EhMTfe5Ls2bNNGHCBH333Xfq1KmTJOmJJ55Qbm6ux+tmzpypqKgoRUVFKT4+3ud2AQBA7VM6AHWxBfxI5qxZszRr1qyL0tbQoUO1efNmPffcc5o2bZrmzp2rTp066d577y0bau7bt2+l9aSkpOiFF14oG5msqlatWmndunW66qqrdObMGX3yySe666673JafMmWKHn/8cUnmm+pHjhzR1VdfrePHjysqKqpafYG9cnJy1KpVK6Wmpro8mgH/4b4ELu5N4OLeBCbDMHTq1CnFx8erYcOGfulDwIfMiy0hIUGrVq0qd2z79u1KSUmRJI0aNcrj9U6nU4mJiXr55ZfVtGnTavenTZs2euyxx/SXv/ylrA/uhIaGKjQ0tOzfY2NjJUmRkZGKiIiodl9gn5KSEklSREQE9yaAcF8CF/cmcHFvApfT6ZSkSt8LqSkBP10eCF588UVJUvfu3TVkyBCPZZOSkpSUlKQRI0a4TOOXLgi/YcOGsmPeKF0+yR/PUwAAAFQFI5mV2LJli5YtW6agoCAtWLCg0mAYGhpa9hxlRQUFBTp27JjCw8PVrl07r/vQsmVLSdJ1113nfccBAAD8iJDpQXZ2thITE2UYhp5//nn169ev0mv69Omj/fv3W55bv369Bg0apD59+mj9+vVe92PPnj3q16+fEhISvL5GMgPvn/70p3JT6AgM3JvAxH0JXNybwMW9CVz+vjd1Zrq8dM/x0mdHKpOZmamhQ4fq0KFDmjFjhtt1KidPnqz+/ftr79691epfUVGRcnJyLI8vWLBA//znP32uMzQ0VM888wx/8AMQ9yYwcV8CF/cmcHFvApe/702dCJlOp7Nsp56tW7eWPQhrJSMjQ/PmzVO3bt105MgRffjhh5oyZYpl2fT0dM2ZM0dJSUlauHBhtfqYkJCgZs2a6bnnnlNBQYEk6YcfftCjjz6q2bNnq3PnztWqHwAA4GKq9SHzySefVHR0tD799FNJ0ieffKLo6GhNnTq1XLmCggI1a9ZM8fHxWrFihSZNmqSDBw9q2LBhbutu0qSJRowYoZiYGN1xxx3V6ue0adN0+eWX6y9/+Ys6duyoX/7yl/rggw/00ksv+TxNDgAA4G8Ow197DQEAAKDWqvUjmZeq8+fPa9asWerUqZPi4uJ04403auPGjT7VkZeXp0mTJqljx44KCQlRmzZt9Pvf/14nTpxwe83Ro0f14IMPKjY2Vi1btlSXLl30yiuveP0sa11gx70pKSnRK6+8oi5duig8PFzt27fXlClTKt2V4d1339U111yj2NhYde/evUrP6tZW/rwvkrRr1y7dc889GjNmTFW/Qq3lr3vzxRdf6Oabb1bDhg0VERGhfv36afny5dX9OrWKv+7N/v37dfvtt6t58+aKjIzUoEGDtGXLlup+nVrF3z/TSv3444+Kjo6u0m6KMhBwzp07ZwwaNMi46qqrjB9++MEwDMNYsWKFUb9+fWPFihVe1ZGbm2tcffXVhiQjODjYcDgchiRDktGiRQvjwIEDLtd8++23RuPGjY0nnnjCKCwsNAzDMJYvX27Ur1/fuPvuu42SkhL7vuQlyo57YxiG8cADDxjBwcFG27Ztjfr165fdm1/96ldur5kyZYoRGRlprF+/3jAMw9i3b59x2WWXGY8++mj1vlQt4M/78t///te46667yv6MJSYmVvfr1Cr+ujdLly41goKCDElGvXr1yspLMh5//HFbvtulzl/3Zvfu3UbDhg2N6Ohoo1mzZmXlw8LCjO3bt9vx1S55/vyZdiGn02n87Gc/q/LPNkJmAPrDH/5gSDL++9//ljt+zz33GA0aNDC+//77SuuYNGmS0bNnT+Pzzz83CgsLjdzcXGPOnDllP2z79u1brnx+fr7Rvn17o0+fPi51Pffcc4Yk4/nnn6/eF6sF7Lg3y5cvN+6++27jxIkThmEYRl5envHggw+W/eHfuXOnyzWrVq0yJBmzZ88ud3zRokWGJOPtt9+uxre69PnrvhiGYXz//fdGSUmJkZiYSMi04I97k5aWZkRFRRnTpk0ru+bbb781evfuXXbNJ598YtM3vHT5496UlJQY/fv3N959913D6XQahmEYGzduNC677DJDknH77bfb9O0ubf78mXahefPmGZGRkYTM2uLw4cNGvXr1jKuuusrl3EcffWRIMkaNGuWxjuLiYuO6664zzpw543Ju2rRpZf+BpaSklB0vDSvPPPOMyzVnzpwx6tevbzRu3NjIzc31/UvVEnbcG8MwjDlz5riMChcVFRmxsbGGJJffUktKSozLL7/ccDgcxqlTp8qdy83NNYKDg43WrVsbxcXFVfhWlz5/3ZeK/v73vxMyK/DXvfnrX/9qzJs3z6WetLQ0IyYmxpBk/OY3v/Hx29Qu/ro3W7ZsKZuNudC//vUvQ5Jlf+qaQPmZduDAAaNdu3bGzJkzq/yzjWcyA8zbb7+t4uJiy4Xfr732WknSqlWrlJGR4baOkydPavLkyWrcuLHLuQvX+zx9+nTZP3/22WeSpFatWrlc07hxYyUkJCgrK0sff/yx19+ltrHj3kjmigcV95GtV69e2SoC3bt3L3du69atOnjwoOLi4tSsWbNy5yIjI9WlSxcdP35cH330kc/fqTbw132pKCwszJdu1wn+ujchISEaN26cSz2XXXaZEhMTJZX/+VcX+eveXHfddbrxxhtd6ind0a5nz55ef4faKhB+ppWUlCgxMVEvvfSSWrRo4etXKEPIDDCrV6+WJMXGxrqci4mJUevWrXX+/HklJSW5raN169a67bbbLM9FRUWVBZULt7bMzMyUZO5yZKV9+/aSVLbeaF1kx73x5OTJk/r1r3/tsi2pp3YlqVu3bpLMlxzqIn/dl4oq23K2LvLXvRk3bpzLX66lLr/8ckn/9zOtrgqUPzcXlg8NDdVTTz1VpfZqk0C4N7Nnz1Z8fLzuvPPOKrVRipAZYLZv3y5JatOmjeX50tHJHTt2VKn+4uJiZWVlqU+fPmV7oktmMJXch0jjp5Wu0tPTq9RubVCT92bbtm1luztdzHZrA3/dF1QuEO9N6c+wkSNH+txmbRJo9+bvf/+7/vWvf5X90lyX+fve7Ny5U6+99prmzp3rc/0VETIDyLlz55SXlydJllPdkjkSKVU97H355Zc6f/68nnzyyXLHSxeT/+CDD3T06FGX644fPy7JnIaqi2ry3nz88ccaPny4rrjiCuXn57ucL53Wq6n/Ji5l/rwv8CxQ7826devUrVs3DRkyxKfrapNAujdnzpzRAw88oKSkJB45kf/vzfnz55WYmKhXX321rJ3qIGQGkAufr2jQoIFlmdIpoHPnzlWpjblz5+qWW27RXXfdVe74iBEj9Jvf/Ebnzp3TL37xi7K92I8cOaLJkyeXjXDW1Smmmrg33333ne655x7deuutOnnypN544w316tVL+/bts2y7pv6buJT5877As0C8Nzt37tSmTZs0f/58t9PpdUGg3Ju//vWv6t27t5YsWaKTJ0/qrrvu0v/7f//P+y9SC/n73vzxj3/UTTfdpEGDBlWh9xZ9taUW2OLCUULDzUZM58+fl2Q+l+Gr9evXa9OmTVqyZInl+ddff11///vfFRYWpiFDhuimm27SggUL1Llz57J2+/bt63O7tUFN3JurrrpKb731ltLS0vTcc88pLCxMJ0+e1EMPPWTZdk38N3Gp8+d9gWeBeG8mTJigyZMna8CAAV6Vr60C5d78v//3/3To0CFt3Lix7O+Wv/3tb1q7dq23X6XW8ee92bx5sz766CPNmDGjir13RcgMIDExMWX/gbkbys7KypIkNW3a1Ke6z5w5o4cfflgrV64se/6yIofDoXHjxunrr7/Wjz/+qM8//1yzZ88uez6kVatWdfaHc03em+joaP3P//yP3nnnHUnmH/QLH1kofbPP7nZrA3/eF3gWaPfmb3/7myIjI/Xss8/61FZtFEj3xuFwaMCAAdqwYUPZG+ZvvfWWT23WJv66N/n5+XrooYe0ZMkSWx9bIGQGkODgYF111VWSpNTUVMsyp06dkuTbMg8lJSW6//779eyzz6p///4+9Sk9PV1vvPGGJHP5o7o6xVRT9+ZCv/jFL9SnTx+XNkqXmaipdi9l/rwv8CyQ7s2GDRu0atUqLV++XMHBwVVqqzYJpHtTqn79+vrjH//odfnayl/3ZuXKldq3b58SEhLkcDjKfR544AFJ5mynw+FQhw4dvG6rbiaGAFb6MHrpM5EXSk9PV3Z2tiIiInwaURw3bpxGjhxZpaUIpk6dqqysLHXr1k2PPvqoz9fXJjVxbyoq/SXgwjf/PbUrSYcOHZIkDR06tMrtXsr8dV9QuUC4N3v37tW0adO0atUqt8+41UWBcG+qW7628se9iYyMVKdOnSw/pbNpjRo1KttH3VuEzAAzZswYBQUFaePGjS7ntmzZIkm68847FRoa6lV9EydO1OWXX275XExGRoZycnLcXrt69Wq9+uqriomJ0b///W/Vr1/fy29RO9l9b6xkZ2erR48e5V6wuuWWW9SxY0ft27fPZQHprKws7du3Tx07diybaqpr/HVfUDl/35sDBw5o3LhxWrFihaKjo13OHz58uMrtXur8fW/clZdYXsof9+b222/X/v37LT8zZ84sV6Z08xav+LxHEGrc73//e0OSsX379nLH77zzTiM8PLzcdpCff/650adPH+Nvf/ubSz1PPPGE8ec//9myjV27dhkDBgww8vLyLM+vXbvWiIiIMJo3b+7Sj7rMrntjJSMjw2jZsqWxbt06l3PLly83JBkvv/xyueNz5841JBnLli3z+bvUJv66Lxf6xz/+YUgy7rvvPp/7X5v5694kJycb119/vfHDDz+4nCsoKDBefPFFY/r06b59mVomEP7cXOgvf/mLMXDgwLI9zeuyQLo3r732GnuX1yZ5eXlGQkKCce211xoZGRmG0+k0XnnlFSMkJMR45513ypUdPny4IcmIjIwsO+Z0Oo2HH37YcDgcRpMmTcp9YmJijPDwcEOSce+997q0vX37duP3v/+9ERwcbPz85z83UlNTa/z7Xkqqe29Onz5txMbGGl27djUWL15snDt3zjAMwzh06JBx8803ewyLY8eONZo0aWLs3LnTMAzD2Lhxo9GoUSNjwoQJNfBNLy3+vC+lHnroIUOS0a1bN+Ps2bP2fsFLmD/uzc6dO43mzZsbERERLj8DGzdubAQFBRmSjIMHD9bslw9w/rg3M2fONJo0aWKMHj3a2Ldvn2EY5n7af//7343BgwcbmZmZNfiNLx2B8DOtFCGzFsrJyTH+8Ic/GB07djTi4uKMkSNHloWLC/3v//6v0bBhQ+ORRx4pOzZp0iRDUqWfjz76qOyaJUuWGA0bNjTatGljjBo1yvjkk08uyve8FFXn3hQVFRkPPPCA0aRJEyMoKMi47LLLjMGDBxtPPPGEcfToUY/tOp1OY+7cuUbnzp2N2NhYo2/fvsZ7771n+/e7VPnrvvz1r381YmJiyv3ZioiIMO6//37bv+Ol6mLem+PHjxvR0dGV/vzr06dPjX7nS8XF/nPz+eefG127djVCQ0ONkJAQo0ePHsaoUaOM5cuXM4JZgb9+plVUnZDpMAw3CzEBAAAAVcSLPwAAALAdIRMAAAC2I2QCAADAdoRMAAAA2I6QCQAAANsRMgEAAGA7QiYAAABsR8gEAACA7QiZAAAAsB0hEwAAALYjZAIAAMB2hEwAAADYjpAJAAEoMzNTs2fP1sCBA9WyZUuFhoaqadOmSkhI0KRJk3TgwAF/dxEAPHIYhmH4uxMAgP/zr3/9S48//rhycnIkSQ6HQ40bN1ZeXp6KiookScHBwRo/frxeeOEF1atXz5/dBQBLjGQCQAD585//rIceekg5OTnq37+/1qxZo7NnzyozM1OFhYX65ptv9OCDD8rpdOpvf/ubfvGLX6i4uNjf3QYAF4xkAkCA+M9//qORI0fKMAw98sgjeuWVVxQUZD0WsGLFCt1zzz1yOp164okn9Pzzz1/k3gKAZ4RMAAgAJSUl6tKli5KTk3X99ddr48aNbgNmqf/5n//RjBkzVK9ePSUnJys2NvYi9RYAKsd0OQAEgC+++ELJycmSpGeeeabSgClJkyZNUkREhIqLi/XPf/6zprsIAD4hZAJAAPjss88kSZGRkRo4cKBX10RFRWnAgAHlrgeAQEHIBIAAkJKSIkmKj4/36W3xTp06SZIOHz5cI/0CgKoiZAJAAMjKypJkjmT6orR8dna23V0CgGohZAJAAIiJiZEknTlzxqfrSss3bdq03PGSkhLNnj1b8fHxCg0NVVxcnJ577jmWOwJw0RAyASAAtG3bVpJ04MABnTt3zuvrdu3aJUlq06ZNueOPPvqonnrqKSUkJGj+/Pn62c9+pj/+8Y8aO3asfZ0GAA8ImQAQAH7+859LkoqKirRmzRqvrklLS9OWLVskSUOGDCk7vnv3bi1cuFCjRo3S22+/rYceekgLFy7Uk08+qcWLF+ubb76x/wsAQAWETAAIAIMGDSobzfR2YfWXX35ZJSUlcjgcuueee8qOL1++XIZh6LHHHitXvvTfly9fXu74mDFj1KBBAx07dkyJiYlq0qSJoqOj9fvf/17FxcXKy8vTxIkT1bZtWzVs2FC33XabMjIyqvN1AdQBhEwACABBQUGaPXu2JGnLli2aNWuWx/JffvmlXnzxRUnSgw8+qM6dO5ed++abbxQUFKTevXuXu6Z169Zq3bq1y0jmjh07FBUVpRtuuEGRkZGaMWOGEhIStGjRIs2ePVvXX3+9jh07pqefflp33XWX3n//ff3pT3+y42sDqMXY8QcAAsiYMWO0ePFiBQUFaeXKlRo5cqRLmZSUFPXr109paWnq2rWrNm/erIYNG5ad79atm9LS0nTq1CmXa/v06aMzZ87o4MGDkqTi4mJFRkaqpKRE69at04033ihJysnJUXR0tJxOp+bNm6dHHnmkrI5OnTqpYcOGTLsD8IiRTAAIIAsXLtQdd9whp9OpO+64Q5MnT1ZRUVHZ+TfffFO9evVSWlqaOnXqpI8//rhcwJSkgoIChYaGWtYfFhams2fPlv37vn37VFhYqN/+9rdlAVOSGjZsqPr16+u6664rFzAlqVGjRgoODrbj6wKoxQiZABAAWrRooRYtWqht27bauHGjJMnpdGrOnDl66623JElHjx7Vfffdp5ycHEnSiRMnlJCQUHbt5s2bJZlBsrCw0LKdc+fOKSwsrOzfd+zYIUm6++67y5U7ePCgCgsLdeedd5Y7bhiGDhw4oCuvvLL6XxpAreb9thIAgBpjNbVdyul0lvvfUjk5OWWBU5LOnz8vyVzOaP/+/Tp//rxCQkLKXZOamqq4uLiyf9+xY4ccDofL85vbt2+XZE6vX+j7779XTk6Orr76am+/GoA6ipAJAAHAm8fjO3To4FW5hIQEffrpp/rmm2/Ur1+/suPHjx/X8ePHNWrUqLJjO3bsUHx8vMuUe2n47Nmzp8txSS7HAaAipssBoJYZNWqUHA6HXnnllXLHS//9wpC5c+dOy1HJHTt2KC4uTo0aNXI5LhEyAVSOkUwAqGV69Oih3/3ud1q0aJEMw9DgwYP1zTffaNGiRUpMTCybAj927JgyMjIsQ+b27dt1ww03WB5v3769GjduXNNfA8AljpAJALXQvHnz1L59e/3zn//Ue++9p1atWumZZ57RlClTysrs3LlTkuuo5KlTp3Tq1Cn16tXLpd4dO3a4PL8JAFZYJxMAAAC245lMAAAA2I6QCQAAANsRMgEAAGA7QiYAAABsR8gEAACA7QiZAAAAsB0hEwAAALYjZAIAAMB2hEwAAADYjpAJAAAA2xEyAQAAYDtCJgAAAGz3/wFHVIhy2AdNXwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Contours without Riess point\n",
    "#7)Contour om - Μ\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from joblib import Parallel, delayed \n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "om_vals7 = np.linspace(0.29, 0.34, 150) \n",
    "M_vals7 = np.linspace(-19.4, -19.45, 150)\n",
    "OM_grid7, M_grid7 = np.meshgrid(om_vals7, M_vals7)\n",
    "\n",
    "params7 = [(om, M) for om, M in zip(OM_grid7.ravel(), M_grid7.ravel())]\n",
    "results7 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total)(M, om, wowwcdm, wawwcdm, hwwcdm) for om, M in params7)\n",
    "Z_7 = np.array(results7).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_wwcdm \n",
    "levels7 = [\n",
    "    chi2_min + dchi(1,2),  # 1 sigma \n",
    "    chi2_min + dchi(2,2),  # 2 sigma \n",
    "    chi2_min + dchi(3,2)  # 3 sigma \n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour7 = ax.contour(OM_grid7, M_grid7, Z_7, levels=levels7, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(omwwcdm, Mwwcdm, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r'$M$')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0534d851-8024-44fa-97b0-d363ea403a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#8)Contour om - h\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from joblib import Parallel, delayed \n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "om_vals8 = np.linspace(0.29, 0.34, 150) \n",
    "h_vals8 = np.linspace(0.65, 0.69, 150)\n",
    "OM_grid8, h_grid8 = np.meshgrid(om_vals8, h_vals8)\n",
    "\n",
    "params8 = [(om, h) for om, h in zip(OM_grid8.ravel(), h_grid8.ravel())]\n",
    "results8 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total)(Mwwcdm, om, wowwcdm, wawwcdm, h) for om, h in params8)\n",
    "Z_8 = np.array(results8).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_wwcdm \n",
    "levels8 = [\n",
    "    chi2_min + dchi(1,2),  # 1 sigma \n",
    "    chi2_min + dchi(2,2),  # 2 sigma \n",
    "    chi2_min + dchi(3,2)  # 3 sigma \n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour8 = ax.contour(OM_grid8, h_grid8, Z_8, levels=levels8, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(omwwcdm, hwwcdm, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r'$h$')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "27f9cbf0-99fa-429f-bf6d-80da5e66ccee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#9)Contour M - h\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from joblib import Parallel, delayed \n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "M_vals9 = np.linspace(-19.4, -19.45, 150) \n",
    "h_vals9 = np.linspace(0.65, 0.69, 150)\n",
    "M_grid9, h_grid9 = np.meshgrid(M_vals9, h_vals9)\n",
    "\n",
    "params9 = [(M, h) for M, h in zip(M_grid9.ravel(), h_grid9.ravel())]\n",
    "results9 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total)(M, omwwcdm, wowwcdm, wawwcdm, h) for M, h in params9)\n",
    "Z_9 = np.array(results9).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_wwcdm \n",
    "levels9 = [\n",
    "    chi2_min + dchi(1,2),  # 1 sigma \n",
    "    chi2_min + dchi(2,2),  # 2 sigma \n",
    "    chi2_min + dchi(3,2)  # 3 sigma \n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour9 = ax.contour(M_grid9, h_grid9, Z_9, levels=levels9, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(Mwwcdm, hwwcdm, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$M$') \n",
    "ax.set_ylabel(r'$h$')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "86f68c33-d4c7-447c-b9ef-77e06e62962f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#10)Contour om - w0\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from joblib import Parallel, delayed \n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "om_vals10 = np.linspace(0.29, 0.34, 150) \n",
    "wo_vals10 = np.linspace(-1.10, -0.95, 150)\n",
    "OM_grid10, wo_grid10 = np.meshgrid(om_vals10, wo_vals10)\n",
    "\n",
    "params10 = [(om, wo) for om, wo in zip(OM_grid10.ravel(), wo_grid10.ravel())]\n",
    "results10 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total)(Mwwcdm, om, wo, wawwcdm, hwwcdm) for om, wo in params10)\n",
    "Z_10 = np.array(results10).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_wwcdm \n",
    "levels10 = [\n",
    "    chi2_min + dchi(1,2),  # 1 sigma \n",
    "    chi2_min + dchi(2,2),  # 2 sigma \n",
    "    chi2_min + dchi(3,2)  # 3 sigma \n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour10 = ax.contour(OM_grid10, wo_grid10, Z_10, levels=levels10, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(omwwcdm, wowwcdm, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r'$w_{0}$')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "66ef7c10-5751-4854-90cd-2b54167ec31c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#11)Contour Μ - w0\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from joblib import Parallel, delayed \n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "M_vals11 = np.linspace(-19.4, -19.45, 150) \n",
    "wo_vals11 = np.linspace(-1.10, -0.95, 150)\n",
    "M_grid11, wo_grid11 = np.meshgrid(M_vals11, wo_vals11)\n",
    "\n",
    "params11 = [(M, wo) for M, wo in zip(M_grid11.ravel(), wo_grid11.ravel())]\n",
    "results11 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total)(M, omwwcdm, wo, wawwcdm, hwwcdm) for M, wo in params11)\n",
    "Z_11 = np.array(results11).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_wwcdm \n",
    "levels11 = [\n",
    "    chi2_min + dchi(1,2),  # 1 sigma \n",
    "    chi2_min + dchi(2,2),  # 2 sigma \n",
    "    chi2_min + dchi(3,2)  # 3 sigma \n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour11 = ax.contour(M_grid11, wo_grid11, Z_11, levels=levels11, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(Mwwcdm, wowwcdm, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$M$') \n",
    "ax.set_ylabel(r'$w_{0}$')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "241cfdf4-6d81-411b-9209-b166c0dbc590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#12)Contour h - w0\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from joblib import Parallel, delayed \n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "h_vals12 = np.linspace(0.65, 0.69, 150) \n",
    "wo_vals12 = np.linspace(-1.10, -0.95, 150)\n",
    "h_grid12, wo_grid12 = np.meshgrid(h_vals12, wo_vals12)\n",
    "\n",
    "params12 = [(h, wo) for h, wo in zip(h_grid12.ravel(), wo_grid12.ravel())]\n",
    "results12 = Parallel(n_jobs=-1, prefer=\"threads\")(delayed(chi2total)(Mwwcdm, omwwcdm, wo, wawwcdm, h) for h, wo in params12)\n",
    "Z_12 = np.array(results12).reshape(150, 150)\n",
    "\n",
    "#Contour definition\n",
    "chi2_min = chi2_wwcdm \n",
    "levels12 = [\n",
    "    chi2_min + dchi(1,2),  # 1 sigma \n",
    "    chi2_min + dchi(2,2),  # 2 sigma \n",
    "    chi2_min + dchi(3,2)  # 3 sigma \n",
    "]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "contour12 = ax.contour(h_grid12, wo_grid12, Z_12, levels=levels12, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "\n",
    "ax.plot(hwwcdm, wowwcdm, 'ko', markersize=8)\n",
    "\n",
    "ax.set_xlabel(r'$h$') \n",
    "ax.set_ylabel(r'$w_{0}$')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "acad04c0-44e4-46f4-8c9f-2ab160af6b45",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Αποθήκευση σε ξεχωριστό κελί\n",
    "np.savez('contour_data_wcdm_21.npz', OM_grid1=OM_grid1, M_grid1=M_grid1, Z_1=Z_1,\n",
    "                                  OM_grid2=OM_grid2, h_grid2=h_grid2, Z_2=Z_2,\n",
    "                                  M_grid3=M_grid3, h_grid3=h_grid3, Z_3=Z_3,\n",
    "                                  OM_grid4=OM_grid4, wo_grid4=wo_grid4, Z_4=Z_4,\n",
    "                                  M_grid5=M_grid5, wo_grid5=wo_grid5, Z_5=Z_5,\n",
    "                                  h_grid6=h_grid6, wo_grid6=wo_grid6, Z_6=Z_6,\n",
    "                                  OM_grid7=OM_grid7, M_grid7=M_grid7, Z_7=Z_7,\n",
    "                                  OM_grid8=OM_grid8, h_grid8=h_grid8, Z_8=Z_8,\n",
    "                                  M_grid9=M_grid9, h_grid9=h_grid9, Z_9=Z_9,\n",
    "                                  OM_grid10=OM_grid10, wo_grid10=wo_grid10, Z_10=Z_10,\n",
    "                                  M_grid11=M_grid11, wo_grid11=wo_grid11, Z_11=Z_11,\n",
    "                                  h_grid12=h_grid12, wo_grid12=wo_grid12, Z_12=Z_12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "bb19dbd8-8ddf-4b87-baad-a286bae1fd69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Όλα τα δεδομένα φορτώθηκαν με επιτυχία!\n"
     ]
    }
   ],
   "source": [
    "#ενωση αρχειων 7-12(μονο τα χωρις Riess)\n",
    "data = np.load('contour_data_wcdm_21.npz')\n",
    "# Contour 7\n",
    "OM_grid7 = data['OM_grid7']\n",
    "M_grid7 = data['M_grid7']\n",
    "Z_7 = data['Z_7']\n",
    "\n",
    "# Contour 8\n",
    "OM_grid8 = data['OM_grid8']\n",
    "h_grid8 = data['h_grid8']\n",
    "Z_8 = data['Z_8']\n",
    "\n",
    "# Contour 9\n",
    "M_grid9 = data['M_grid9']\n",
    "h_grid9 = data['h_grid9']\n",
    "Z_9 = data['Z_9']\n",
    "\n",
    "# Contour 10\n",
    "OM_grid10 = data['OM_grid10']\n",
    "wo_grid10 = data['wo_grid10']\n",
    "Z_10 = data['Z_10']\n",
    "\n",
    "# Contour 11\n",
    "M_grid11 = data['M_grid11']\n",
    "wo_grid11 = data['wo_grid11']\n",
    "Z_11 = data['Z_11']\n",
    "\n",
    "# Contour 12\n",
    "h_grid12 = data['h_grid12']\n",
    "wo_grid12 = data['wo_grid12']\n",
    "Z_12 = data['Z_12']\n",
    "\n",
    "print(\"Όλα τα δεδομένα φορτώθηκαν με επιτυχία!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3d667b16-9b7a-4521-a2f7-515b3a3d3b17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BgEf1ImQ6nc7ynXp27Nghp9PptmxmZqYWLlyonj176sSJE3r//fc1bdo0y7IZGRmaN2+ekpOTtWTJEtv6m5mZqaNHj0qSPv30U9vqBXBt2r9fio6W7FiZ7PvfZ/cfAFdHnQ+ZTz/9tKKjo7Vx40ZJ0oYNGxQdHa3p06dXKFdYWKiWLVsqISFBq1ev1pQpU3TkyBGNHDnSbd3NmjXT6NGjFRMTo7Fjx9rS30GDBqldu3Y6d+6cJGn69Olq1aqV3udvBaDe+uQT6dvftqeu226Ttm+3py4A8MRh8HpznVRQUKCIiAhJUn5+vsLDw/3cIwDVNXasdO+90l131byu8+fNt8zPneO5TKAuC4QcUOdHMgHgWuZ0Slu3SkOG2FNfixZS48bSqVP21AcA7hAyASCAffGF+SymnXs7JCaa21MCQG0iZAJAAPvkE3PZITsRMgFcDYRMAAhgmzebL+vY6aabCJkAah8hEwAClNMpbdsmfetb9tZ7003Srl321gkAlREyASBAHTpkvqhj5/OYktSypRQcLJ05Y2+9AHAlQiYABKg9e6Qbb6ydum+8Udq5s3bqBgCJkAkAAeuLL6RevWqn7j59zBALALWFkAkAAeqLL6QePWqn7t69CZkAahchEwAC1JdfSj171k7dffpIu3fXTt0AIBEyASAg5edLWVlSx461U398vPniT2Fh7dQPAIRMAAhA+/dLXbvW3v7iQUFm/fv21U79AEDIBIAA9OWXtfc8ZplevaS9e2u3DQD1FyETAALQvn1S9+6120bPnubLRQBQGwiZABCA9u+v/ZDZowfT5QBqDyETAALQ1RjJ7NHDnJYHgNpAyASAAJOXZ37atKnddlq2lC5flnJyarcdAPUTIRMAAsyBA76/Wb5vn7nska86d5ZOnPD9OgCoCiETAAJM2fJF3vrPf6RvfUu6/nppwQKpqMj7a2NjpePHfe4iAFSJkAkAAaZsJNMbp09Ld90lrVolffSR9MknUrdu0qZN3l3fuTMhE0DtaODvDgAAKjpwwByZrMqlS9IPfyhNnix95zvmsTVrpI0bpZ/+VDp6VAoO9lxHbKx05EiNuwwALhjJBIAAc/CgdMMNnssYhvSzn5mjlr/4RcVzw4ZJnTpJ69ZV3VanTtLJk9XvKwC4Q8gEgABy+bKUlmaOMHqycaO0Z4+0ZIn1C0I//7m0aFHV7cXG8uIPgNpByASAAHL0qBQXV/U09+rV0iOPSI0aWZ//wQ/M3XyOHfNcT8eO0ldfVaurAOARIRMAAsihQ1KXLp7LlJRI//yn+TymOw0bSg88IP3hD57ratpUKi2t3vJHAOAJIRMAAsjBg1WHzK1bzeWKqlqs/ac/lZYvl4qLPZfr2FE6dcqnbgJAlQiZABBAUlOl667zXGbNGmns2Krr6txZ6tmz6heAOnQgZAKwHyETAALIyZPmG9/uOJ3Su+96niq/0kMPScuWeS7ToQPPZQKwHyETAALIV1+Zoc+d//5Xat7cHKX0xg9+IG3fLmVkuC/DdDmA2kDIBIAAYRjS119L7du7L/Puu2Zw9FajRtItt5hbT7rTvr25cxAA2ImQCQABIjPTfNs7NNR9mffek8aM8a3efv2kHTvcn2/f3gy3AGAnQiYABIhTpzxPlaemSrm5Up8+vtV7881Vj2QSMgHYjZAJAAGiqqnyf/5T+v73rXf48eTmm81nOQ3D+ny7dkyXA7AfIRMAAsTXX3seyfzoI2nECN/rbd5cCgtz/3JPeLgZQAsLfa8bANwhZAJAgKhqJHPXLnNUsjoSE83RTHfatjX3TAcAuxAyASBAfP21OXVtJT3dXCOzVavq1Z2YKO3c6f5827ZMmQOwFyETAAKEp5HMvXul3r2rX/eNN5ojoe60bSudOVP9+gGgMkImAASI06fdj2Tu2SP16lX9uqsKmW3aMF0OwF6ETAAIEGlp5oiilb17axYyW7eWSkrMaXcrhEwAdiNkAkAAyMuTGjY03wK38uWXUs+e1a/f4TCn2/futT7fpo107lz16weAygiZABAATp92P4pZWiodPizdcEPN2ujVy33IbN1aOnu2ZvUDwJUImQAQANLSzNFEK6mp5gtBjRrVrI3evc1nO60QMgHYjZAJAAHA0/OYX34p9ehR8zZ69pT27bM+16oV0+UA7EXIBIAAcOaM+5C5b5/UvXvN27jhBunQIXP6vbLISCk/33w5CADsQMgEgABw5oz76fIvv7QnZDZqZC6RdOyY6zmHw9x+8vz5mrcDABIhEwACgqeQuX+/PSFTMuvZv9/6XMuWhEwA9iFkAkAAOHvWOmSWlEjHj0sJCfa0060bz2UCuDoImQAQAM6cMd/wruzYMalDBykkxJ52rrvOfFvdSsuW7hdrBwBfETIBIACcPWsdMg8ckLp2ta+dDh3MPdKttGjBdDkA+xAyAcDPLl0y3/gOD3c9d/CgvSGzXTv3ITM6Wrpwwb62ANRvhEwA8LNz58znIa0cPFjznX6u1K6dubuQlehoKSvLvrYA1G+ETADws6pCZpcu9rUVHm4uV5Sf73qOkUwAdiJkAoCfeQqZhw/bGzIlczQzLc31eFSUlJNjb1sA6i9CJgD4WXq6dcjMyJAaNjR347GTuynzqCgpO9vetgDUX4RMAPCz9HRz+aDKTp6UYmPtb69tW/cjmYRMAHYhZAKAn7kLmadPm6OOdmvd2lyXs7LISKbLAdiHkAkAfpaebq5RWVlamnch0+mUHnxQ+vBD79pr08Y6ZDZtSsgEYB9CJgD4mbuQefq0ObVdlZUrzU9SkvTll1WXdxcyw8LMNTudzqrrAICqEDIBwM8yMqxD5pkzVYfMwkLp//5PSk6WFi2Sxoypetced3uUOxxSkyZSQYH3fQcAdxr4uwMAUN+dP+9+urxNG8/Xzp8v3Xab1Lev+dm/X7rzTmnTJvf7nbsLmZI5ZZ6ba4ZNAKgJRjIBwI8Mw9xlp1kz13NnzngOmWfPSgsWSLNm/e/YjBnmNb/+tfvrWrc2r7XCc5kA7ELIBAA/KiiQGjWSGljMK1UVMmfOlH76U6l9+/8dczjMoPnPf7q/rmlTs92SEtdzTZpIeXne9x8A3GG6HAD8KDvbXJ+ystJSc0QxJsb6uuPHpXfflQ4dcj3XrZv50lBOjvVC7g6H1Ly5lJnpugg8IROAXRjJBAA/ys62DoLnz5tB0OGwvu7jj6WRI81RycqCgsznMz/7zH277p7LJGQCsAshEwD8KDtbio52PX72rPnspDs7d0qJie7P9+sn7djh/nzz5uZb7ZURMgHYhZAJAH7kbiTz3Dnr/czL7Nol3XST+/N9+0qff+7+fIsW1iEzIkLKz3d/HQB4i5AJAH7k7rlJTyGztNRcdL1XL/f19u1b9Uim1XqaERGMZAKwByETAPwoJ8f6xZ/0dPch88gR843yxo3d19u+vbl7j7uF2Zs1M1/8qSwigsXYAdiDkAkAfpSTY/3yTnq61LKl9TV79ki9e3uu1+GQbrxR2r3b+ry7kBkeTsgEYA9CJgD4UW6u9XR5TUOmZIbMXbusz3kayeSZTAB2IGQCgB+528IxPd16q0lJ2rvX8/OYZXr3NstaiYlhJBNA7SJkAoAfuRvJzMhwHzK//FLq2bPqunv2NMtaiYmRLlxwPd64sVRYWHXdAFAVQiYA+FFurvUzmefPW4fM3FzzOc4rt5J0p0sXKTXVevvI6Ghzz/TKwsMJmQDsQcgEAD9yN12ekWE+N1nZ/v3mtpHudgK6UkiI1LGjdPSo67noaPcjmUyXA7ADIRMA/CgvzzVkFhWZ/9uokWv5/ful7t29r79rV+nAAdfjTZuabRtGxeNhYdLFi97XDwDuEDIBwI+sQmZmpvUopmSGzK5dva/fXcgMCrIOlDyTCcAuhEwA8KP8fN9C5oEDvofMgwetz0VGmttaXomRTAB2qTchc9u2bRo5cqRmzpzpsVxJSYnmz5+v3r17KzY2Vm3atNH48eOVlpZW4z7k5eUpLi5OQ4YMsTxfXFys2bNn64YbblBoaKhatWql++67T4cPH65x2wACk9VIZlaW+5B58KBvIfO668wdgqxERprPhF4pLMzcKQgAaqrOh8yPPvpIw4YN0+DBg7V+/XoZlR9AukJRUZFGjRqlFStWaM2aNTpx4oSSk5O1c+dO9e3bt8Zhb9KkSTp+/LjludLSUo0ZM0bPPvusjhw5opKSEqWnp2vlypXq27evUlJSatQ2gMBjGObzl6GhFY9nZZlLDFV26ZK5fmaHDt63UVXIzMmpeKxRI0YyAdijzofM+Ph4rV+/XkOHDq2y7PTp07Vp0yatWLFC8fHxkqS4uDitXbtWGRkZGjdunEqs1gLxwrp167R69Wq3519//XUdPHhQ7777rvLz81VYWKhly5YpPDxceXl5uvfee3X58uVqtQ0gMF28aI4cVn5TPCvLfPu7sqNHpbg483lKb0VFmfVbvUnetKl1yGQkE4Ad6nzIjIuLU3BwsG6++WaP5XJycrRo0SJ17NhRPXr0qHAuNjZWY8aM0e7du7Vq1Sqf+5CZmamJEydq1qxZbsv8/e9/1+bNmzVmzBiFhYUpNDRUDz74oF599VVJ0smTJ7Vt2zaf2wYQuPLzzW0cK3M3knn0qHT99b6307atdO6c6/EmTczp+isFBZkjrB4mfQDAK3U+ZJZpZLUWyBVSUlJ08eJFtW3b1vL8HXfcIUnVCpkTJkzQxIkTXcJrmeLiYv3oRz9Sx44dXc499NBDivnmb5vz58/73DaAwFVQYC5+XtmFC9YjmUeOSAkJvrfTvLm57mZlViFTMqfvy5ZRAoDqqjch01HFysVZ32x9kVN57ugbnTp1kiTt2LHDp3ZXrlyp9PR0TZo0yW2Zhg0b6mc/+5nb8wnf/K1S1gcAdUN+vm8h8+hR8xlLXzVrZu4gVFmTJq4v/khmyGTKHEBN1ZuQWZV27dpJkg4dOqRci5+6ZS8MZVgNB7iRlpamZ599Vn/5y18U5MtDVJVkZGSoTZs26tevX7XrABB4CgutQ2Z2tvksZWVffWXu4OOrZs2sn8mMiLDe3YeRTAB2IGR+Y8CAAWrdurVKSkq0dOlSl/OnT5+WJIWEhHhd5/jx4zVz5swajUAeP35cx44d05NPPlllUC0qKlJubm75B0BgczddnpNjHTIzMqz3M6+Ku33KIyLM0dTKCJkA7BDwIfOZZ56Rw+Hw6fPAAw/43E5oaKiWLVumkJAQzZgxQ8uXL1dxcbHy8vL01ltvae7cuZK8n7JesmSJwsLClJSU5HNfrrRgwQJ169ZNTzzxRJVlZ8+ercjISEVGRrp9thRA4PA0khkZ6Xr8/Hnz+UpfxcSYC7xXRsgEUJsCPmTOmTNHhmH49Fm+fHm12hoxYoRSUlI0YsQIzZgxQ/3799cjjzyipk2blk+XDxgwoMp6UlNT9cILL+j111+vVj/KHD58WH/+85+1cuVKr0ZQp02bppycHOXk5NiyeDyA2lVYaG7jWJmnkczqhMzoaNedfSQz4FptIRkaKhUX+94OAFypgb87EGgSExO1du3aCsd27dql1NRUSdK4ceM8Xu90OpWUlKSXXnpJzavzt8E3Ll++rAceeEBLly5V7969vbomNDRUod+s6hwcHFzttgFcHZ5CZtOmFY+VjSxWXrjdG1FR1iHT3T7lISESy/ICqClCphdefPFFSVKvXr00fPhwj2WTk5OVnJys0aNHuy2zZcuW8rfd3e1A9Pjjj+vuu+/Wj370o2r2GkCgu3jROmTm5rqGTHdrZ3rD15DZsCEhE0DNETKrsH37dq1cuVJBQUFavHhxlUshhYaGqkuXLpbnCgsLderUKYWFhVmuiVnmueeeU4sWLTwuewTg2ldYaO74cyWn0xy1rLy0b01CptX2kZLnkMl0OYCaImR6kJOTo6SkJBmGoeeff14DBw6s8pp+/frp4MGDluc2b96soUOHql+/ftq8ebNlmQULFujs2bNauHChy7mCggIVFBSoZcuWPn0PAIGpbFvJK7nbBcjd2pneaNrUetH1sDDrfcoJmQDsEPAv/tilbM/x0tJSr8pnZWVpxIgROnr0qGbNmqXJkydblps6daoGDRqkffv21biPCxcu1N69e7VgwQKXc8eOHdOdd96pQqthBwDXJKuRzLw8c5H0ytztZ+4Nd4uuN2pEyARQe+rFSKbT6SzfqWfHjh1yOp1u15zMzMzUW2+9pdmzZ8swDL3//vsaOXKkZdmMjAzNmzdPkrlkkVU49NacOXM0bdo0xcTEuLx4dOnSJRUUFOjWW29VbGxstdsAEFguXXKdFs/Ptw6Z2dk1G8n0NWTyTCaAmqrzI5lPP/20oqOjtXHjRknShg0bFB0drenTp1coV1hYqJYtWyohIUGrV6/WlClTdOTIEbcBU5KaNWum0aNHKyYmRmPHjq12HxcvXqxp06ZJMkdQMzMzK3wKvtmS47777qt2GwACz6VL1tPlViHT3bJG3nA3Ld6okfV6mA0bSl5O+gCAW3V+JPP555/X888/X2W5xo0bKz093ae6HQ6H3nvvPa/LDxkyxPJt8gkTJmjChAk+tQ3g2uduJNPqmcycHOsF2r0RFPS/0ckrl9x1FzIbNJC+ecIIAKqtzo9kAkCgunTJdd1LT1tNVjdkSmadlV/+CQ01+1BZgwY8kwmg5giZAOAnVksVuQuZeXmua2f6onFj1ynzkBDrMBkczHQ5gJojZAKAn1hNlxcUuF+g3epZTW9ZrYnpbmefBg0ImQBqjpAJAH5y6VLFZyQl91tNulvayFtWITM42PrZS0YyAdiBkAkAfnL5suszme5CprsXgrxl9ZJP2QZmld9HDA42dx4CgJogZAKAn1ztkOnuJZ/Ko5ZBQYxkAqg5QiYA+Mnly+bSQley2mpSMkOm1QtB3nK38LrVlDkhE4AdCJkA4CdWI5lWC7RL5ghnTUKmL2+SBwW5TqEDgK8ImQDgJ5UXR5es186UzFFIq2l0b7l7k9zq+UuHg2cyAdQcIRMA/MRqutwqeErun9X0VkiI9e4+VlPjQUGETAA1R8gEAD8pLvYuZBqGdVlfNGxovVyRVaAse+scAGqCkAkAfmIVHK2OlZTULGBK7vcjdzisn7/kmUwANUXIBAA/8TZkWu0M5CtfQiYjmQDsQMgEAD8pKTHD35XchUyrl4F84W53H0YyAdSWBlUXAQDUBquQaTU1brXU0ZUMw1BmZqby8/MVERGhZs2ayVFpONLdskTuQiajmQBqipFMAPATwzDD35WKi12Dp7s3zrOzs/XKK6/ouuuuU4sWLdS5c2e1aNFC1113nV555RVlZ2eXl3X3xjhhEkBtIWQCgB+4m462Gt20Wupow4YNat++vSZNmqRjx45VOHfs2DFNmjRJ7du314YNGyR5Xvuycl+YKgdgB0ImAPiB0+k6iimZa1YGB1c8Vvk5zQ0bNmjUqFG6ePGiDMOQUSkVlh27ePGiRo0apQ0bNrgdsTQM19FMd30DAF/wYwQA/MAqTLo7fuXoZnZ2tu68804ZhiFnFSumO51OGYahO++8U5cuZbstVzlkWgVPAPAVIRMA/KC6IfMvf/mLCgsLqwyYZZxOpwoLC7Vv3xtev0VOyARgB0ImAPiBu5DpdLoeLztmGIYWLFhQrfb++99X5XC4JkpP7QFATRAyAcAP3AU5q+chy8pmZmYqNTXV5RnMqhiGoZycVF26lOV1ezyTCaCm+DECAH7gdFpPSVsdLzuWn59fozaLivJ8ag8AaoKQCQB+4G600Op5yLL1NCMiImrUZnh4E8t+VB5RdTeVDwC+IGQCgB9YLcTu7njZ7HizZs0UHx/vsptPVRwOh5o2jVfTpjEu50pLXdsjZAKwAyETAPzA3ZS0pze7HQ6HHn/88Wq116PHE2rQwLVinskEUFv4MQIAfuLLgGTZaGZSUpIaN26sIC9TYFBQkBo3bqzY2Pstt6Z0t3965WMA4CuvQ+b06dNr1FBNrweAusSXF8Qdjv+Vj4qK0t///nc5HI4qg2ZQUJAcDofWrFkjhyPKZWtKyQyUntblBIDq8jpkVndtNslcPuO1116r9vUAUBd5O5IZFFRx3/Hhw4dr3bp1CgsLk8PhcHlGs+xYWFiYPvjgAw0bNsxla0rpf1PzlfvBM5kA7OD176p5eXl64IEHdOeddyo8PNzrBi5duqR//vOfysnJqVYHAaC+qTzKWTlkSmbQ/Prrr/XGG2/o1VdfVWpqavm5uLg4PfHEE0pKSlJkZKQkqahILtPlpaXWI5ZWgRQAfOXThMiKFSu0YsWK2uoLANR7V06NlwkONgNhZVFRUXriiSf0+OOPKysrS3l5eWrSpIliYmJcRjeLiqTQ0IrXX77sekwyp8sJmQBqyqeQ6esuE1fydckNAKiPrH5UuguZ/7vGoWbNmqlZs2Zuy1gFysuXXUc3JZ7JBGAPr5/JTElJ0Q9/+EMFBQVp0KBBeu+99+R0Oqv85Ofn64033qjN7wAA1ySr39sdDtep8YYNzSnsmrh0SWrUqOIxqyl0yWyLkAmgprwOmf3799ff//53HTlyRH369NG9996rrl276o9//KMuX77s9rrGjRvrxz/+sVq2bGlLhwGgLrCaFpfMUcvaCJkXL0qNG1c8ZhU8JfcjnADgC5/XyezcubNeffVVnTp1Svfff79+/etfq2PHjnruueeUlZXl9rrly5fXpJ8AUKe4C5lWL/nYFTLDwioeu3TJ+plMXvwBYIdqL8YeFRWladOm6cSJE5ozZ47eeecddezYUY8//riOHz/uUn748OE16igA1CVWYbLseOXnL0NCzNHFmigstA6ZjGQCqC013vGnYcOGeuCBB7Rnzx6tWbNGR44cUZcuXXTXXXfp008/taOPAFDnBAW5ny6vHDJDQ83nJ2uisFCqvPqc1eim5P6tcwDwha3bSg4bNkwffvihdu7cqYiICN16660aPHiw3nvvPTubAYBrnruRzNoKmQUF1iGTkUwAtcX2vcsvX76slJQUpaSkyDAMpaSkaOzYserfv7/dTQHANctqWlwy3+ouKal4rFEjc2q7upxOc9S08i4+7kYy3b11DgC+sG2RigsXLui1117TggULdP78+fLjnTp10i9/+UuNHz/erqYA4JrnbiSzQQPX8NmwoTm6WLYNpK/y811HMSUzZFodt1q4HQB8VeOQefLkSc2fP1/Lli1TYWFh+YLtiYmJeuqpp3T33XcrKMj2AVMAuKa5W2DdaiTT4TBHFouLqzfCmJ8vNWnieryw0HVZI4mQCcAe1Q6Zu3bt0vPPP6+//e1vKi0tlWEYcjgcGjlypJ566ikNGTLExm4CQN3iKWRaLVcUFmaGwuqEzLw8KSLC9TghE0Bt8jlkbty4UfPmzdMnn3wiydxqMiQkRPfdd5+eeuopde3a1fK6/fv3q1u3bjXrLQDUEe5Cprs1McPDzVAYFeV7W7m5UtOmrsetXgaSzKl51skEUFNeh8y//vWveuGFF/TFF1+UT4lHRUXp0Ucf1RNPPKHWrVt7vH7EiBE6efJkzXoLAHWEu2cr3a2JGRFhhsLqyM2VIiNdj1s9q1m2b3l1nv0EgCt5HTLvv/9+ORwOGYah2NhY/fKXv9TDDz+sxlZzLZXs2LFDp0+frlFHAaCuKdun/MrH1j2NZObnV6+dnBz3I5mVxwfc7QIEAL7yabq8bASzuLhYL7/8sl5++eUqr7l06ZLOnTtXrc4BQF1W9ib5lSEzNNQ6ZDZpUrOQaTXNnpcnJSRUPHbpkvWyRgDgK59C5tixY3X//fcr0mrexY2CggKtXbtWy5Yt87lzAFCXlb3kc+Xzj6Gh1mtiNmlihsLquHDBOmRavXXubu1MAPCV1yEzLCxMK1euVEg1Xm0cMWKE3nnnHZ+vA4C6zGpqvFEjM+hV1qSJ+WxldWRnWz+TafXWOSETgF28XsBy0qRJ1QqYkuRwOPSLX/yiWtcCQF3VsKHrmphhYdYjmZGR1Q+ZFy5IMTGux/PyXJ/VJGQCsIvXIfO5556rUUMzZ86s0fUAUNeU7eRzpcaNzaWKKouMNEckqyMrS4qOdj1utbSRu7UzAcBXbMUDAH5iFTLDwqyXKoqMNF/gqQ53I5lWIZORTAB2IWQCgJ+UbRV5JXcjmVFR1R/JzMy0DplWSxsxkgnALoRMAPCT0FDXkczwcOuRzJgYc0SyOjIypBYtXI/n5Li+EORuFyAA8BUhEwD8JCTE3Cf8Su5CZnS0+Wylr5xO6exZ10XXi4vNxeArbx/JSCYAuxAyAcBPrEJmRIT1ousxMdULmWfPSs2auYZJq1FMyQy4hEwAdiBkAoCfNGrkGjKbNLEeyWzWzHy20lfHjknx8a7Hs7Ot3zgvKHBdOxMAqoOQCQB+EhpqPZJptbNPs2bms5W+OnZMiotzPe5pFyCeyQRgB0ImAPhJo0auC6+72z6ycWNz4fbKLwpVJTXVOmRmZ1uHTF78AWAXQiYA+InVdHnTpu73KG/e3PfRzCNHpOuucz2elWW9rJHVfuYAUB2ETADwE6t9yt1Nl0tSy5bS+fO+tXHkiJSQ4HrcU8jkmUwAdiBkAoCfWIXM4GCpQQPXEU7JDJlnz3pfv2FIR4+6D5lWL/4QMgHYhZAJAH4SFub6TKbkfgvJ+HgzNHorPd1so/KuPpIZMps1cz2el8d0OQB7EDIBwE/CwlxHMiXzhRyrkNm1q3TggPf1Hzok3XCD9bnMTOuQyTOZAOxCyAQAP3EXMiMjrfcp79pV2r/f+/oPHZK6dLE+524/c0YyAdiFkAkAfuIuZEZHW4fMHj2kL7/0vv4DB8xgaiUz03o/89xcQiYAexAyAcBPGjc29wqvLCrKXCy9subNzReDzp3zrv79+6Vu3azPnT9v1lcZI5kA7ELIBAA/cRcyo6OtQ6Yk9ewp7d3rXf2eRjIzMlyfySwtlYqLzZ2IAKCmCJkA4CfuQmZMjPn2t5U+faQ9e6quOzfXHJVs29b1XHGxuXNQ5Z198vKs30QHgOogZAKAn4SHm9s4VuYpZN54o7RrV9V179snde8uORyu5zIyzKnyyudycwmZAOxDyAQAP3EXMps1M1/MsdKnj3ch84svzBeFrJw/7/6ln8jIqusGAG8QMgHATyIi3IdMdyOZ118vpaWZ61l6snev+fymlfR065CZk8NIJgD7EDIBwE/Cw63DYvPm5pS2leBgqXdvaedOz3Xv3WuWs3LunNSqlevx7GzzzXYAsAMhEwD8xN1IpqeQKUn9+kmffeb+vGGY0+WeRjKtQmZODtPlAOxDyAQAPwkPN9/orqx5czMIutOvn/Sf/7g/f/KkuQySu6nvc+ekli1dj2dnEzIB2IeQCQB+4m4kMyJCunTJXGrIyoAB0vbt7uv997+lxET358+eldq0cT2enW2GUwCwAyETAPwkKEhq0MBcs/JKDoc5munuDfP27c0yp065njMM6cUXpUcecd/uuXNS69auxy9cIGQCsE+9CZnbtm3TyJEjNXPmTI/lSkpKNH/+fPXu3VuxsbFq06aNxo8fr7S0tBr3IS8vT3FxcRoyZIjbMm+++aZuuukmhYeHq02bNnrssceUbbWJMYA6ISLCesq8ZUvPU+YDB0opKa7H162TQkKk2293f+2ZM9bPZGZmuu4CBADVVedD5kcffaRhw4Zp8ODBWr9+vQzDcFu2qKhIo0aN0ooVK7RmzRqdOHFCycnJ2rlzp/r27avDhw/XqC+TJk3S8ePH3Z6fOXOmfvKTn+j8+fNyOp06e/asFi9erFGjRsnpdNaobQCBqWlT9yHT0x7lgwdLW7dWPGYY0m9/K/3qV9aLsJc5c8Z6utzd+pkAUB11PmTGx8dr/fr1Gjp0aJVlp0+frk2bNmnFihWKj4+XJMXFxWnt2rXKyMjQuHHjVFJSUq1+rFu3TqtXr3Z7/tNPP9XmzZt19OhRnTp1Srm5uZo+fbokKSUlRe+//3612gUQ2Jo2NRdBr6xVK88hc9Qoac0aacuW/x378ENz//FRo9xfV1Jitmc1LV62ExAA2KHOh8y4uDgFBwfr5ptv9lguJydHixYtUseOHdWj0jYZsbGxGjNmjHbv3q1Vq1b53IfMzExNnDhRs2bNclvms88+0wcffKC4uDhJUsOGDfW73/1OgwcPliQdOnTI53YBBL4mTaoXMjt3lv72N2ncOOmjj8xRzJkzqx7FLHuz3KoMI5kA7FTnQ2aZRo0aeTyfkpKiixcvqm3btpbn77jjDkmqVsicMGGCJk6c6BJer/TEE08oLCzM5Xj//v0lSX369PG5XQCBLzLSerq8dWvzLXBPbr1V+sc/pKQk6Re/MEcpR4/2fM2ZM1K7dtbn3O0EBADVUW9CpsPTr/aSsr7Zwy0nJ8fyfKdOnSRJO3bs8KndlStXKj09XZMmTfLpujJnz57VrbfeWh5yAdQtTZuai6BX5k3IlMw1MzdulN57T5o1y/MopmRuSWn1u3RRkTkaavG7LgBUSwN/dyBQtPvmV/tDhw4pNzdXTSutYlz2wlCGp204KklLS9Ozzz6rLVu2KCjI9zx/6tQpffHFF3r//ferDMkArk2RkdYhs00bc9TRGz16SCdOVB0wJenrr61DJlPlAOxWb0YyqzJgwAC1bt1aJSUlWrp0qcv506dPS5JCQkK8rnP8+PGaOXNm+SioLz777DPdfvvtatOmjQoLC726pqioSLm5ueUfAIEvMtL6mUxfQqbkXcCUpNRU6Zv3Gitwt9UkAFRXwIfMZ555Rg6Hw6fPAw884HM7oaGhWrZsmUJCQjRjxgwtX75cxcXFysvL01tvvaW5c+dKkteBccmSJQoLC1NSUpJP/fj666/1yCOP6LbbbtORI0e0fv163XTTTfr3v/9d5bWzZ89WZGSkIiMj3T5bCiCwREaaO+1U1qaNObVtt8OHpeuvdz2enm691SQAVFfAh8w5c+bIMAyfPsuXL69WWyNGjFBKSopGjBihGTNmqH///nrkkUfUtGnT8unyAQMGVFlPamqqXnjhBb3++us+96F9+/ZaunSpMjIytGjRIkVFRSk/P1/333+/it3tMfeNadOmKScnRzk5ObYsHg+g9rkLmU2amMsReTmR4bUjR6TrrnM97m4/cwCoLp7JrCQxMVFr166tcGzXrl1KTU2VJI0bN87j9U6nU0lJSXrppZfUvAYLzoWHh+uxxx7TwIEDdfPNN+vEiRPavn27vvWtb7m9JjQ0VKGhoZKk4ODgarcN4OqJirJ+JlMy3wJPS5MSEuxpq6TEfCYzNtb13LlzTJcDsFfAj2QGghdffFGS1KtXLw0fPtxj2eTkZCUnJ2v06NEu0/hlC8Jv2bKl/FhV+vTpo7Fjx0oSo5NAHRQdbT2SKZkh85vHwW1x/LjUoYO5X3plhEwAdmMkswrbt2/XypUrFRQUpMWLF1cZDENDQ9WlSxfLc4WFhTp16pTCwsLUsWNHr/swaNAgrV69Wm2s9oEDcE2LipIuXLA+1769OfJol0OHJDc/nnT2rHTLLfa1BQCETA9ycnKUlJQkwzD0/PPPa+DAgVVe069fPx08eNDy3ObNmzV06FD169dPmzdv9qkfLVu2LF+YHUDdERXlfiTT7pB58KD7kMlIJgC71Zvp8rI9x0tLS70qn5WVpREjRujo0aOaNWuWJk+ebFlu6tSpGjRokPbt22dbX690+fJlvf3225ozZ07585YA6o7oaOmbvSBctG8vnTplX1sHD0pdu1qfO3PGfKMdAOxSL0Km0+ks36lnx44dcjqdbstmZmZq4cKF6tmzp06cOKH3339f06ZNsyybkZGhefPmKTk5WUuWLKl2/0pLS3XjjTcqISFBL7/8svLz8yVJ586d0//7f/9PSUlJevDBB6tdP4DAVfZM5jcLWFTQoYO9IfPAAemGG6zPnT1LyARgrzofMp9++mlFR0dr48aNkqQNGzYoOjpa06dPr1CusLBQLVu2VEJCglavXq0pU6boyJEjGjlypNu6mzVrptGjRysmJqb85ZzqCA4O1vDhw1VQUKDJkyerQ4cO+va3v63f/e53+v3vf6+nnnqq2nUDCGzBwVJEhPUb5h07Sl99ZU87huE+ZBYVmZ+ICHvaAgBJchiG1e/PuNYVFBQo4pu/MfLz8xUeHu7nHgFwp0cPac0a10XSL140n5PMzpaqsTNtBWfPSomJ1m+rnzwp3X67dPRozdoAEDgCIQfU+ZFMAAh0LVuaO+5UFhYmtW5tLj1UUwcOSN26WZ87c8Z6P3MAqAlCJgD4WatW5tvdVnr2lL74ouZt7NvnOWTyPCYAuxEyAcDPPIXMXr2kvXtr3sa+fVL37tbn0tIYyQRgP0ImAPhZq1bmM5NW7AyZPXpYnyNkAqgNhEwA8LPWrd2HzN69pT17ala/YXieLj99mulyAPYjZAKAn3kKmbGxUkaGlJdX/frT0qTGjc3dhaycPm0u/A4AdiJkAoCftWljvnxjJSjInDKvyWjmF1+YLxC5c/q01K5d9esHACuETADwM08hU5L69JF27ap+/VWFTJ7JBFAbCJkA4GctWkiZmZK7HW8TE6X//rf69e/daz7baSUvzxwtZb8GAHYjZAKAnwUFSc2bS+fPW5+vacjcvduccrfy9dc8jwmgdhAyASAAtGljveWjZO43/tVXUkGB7/WeO2e+VNS1q/X5r7/meUwAtYOQCQABoF0789lIK8HB5jOVu3f7Xu/69dKwYWYdVk6dkjp08L1eAKgKIRMAAkC7duaoojv9+kmffeZ7vevWSaNGuT//9deETAC1g5AJAAGgXTv30+WSdPPNvofM4mLp44+l737XfRlGMgHUFkImAASA9u09h8xbbvE9ZP773+YuPzEx7ssQMgHUFkImAASA9u09T5d37mwuN5Se7n2d//yn9P3vey7z1VdSx47e1wkA3iJkAkAA6NDBc8h0OKRbb5U++cS7+gzDDJmjR3suc+oUSxgBqB2ETAAIAO3amYHPMNyX+fGPpT/+0bv6Dhwwg2mXLu7LnD1r7mceFuZTVwHAK4RMAAgAYWHmJzvbfZnRo83weOhQ1fW9+640ZowZNN05dMhzCAWAmiBkAkCA6NDBHM10p2FD6ac/lZYsqbqu994zQ6Ynhw5J11/vWx8BwFuETAAIEB07mi/iePLTn0pvvinl57svk5YmnTghDRzouS5GMgHUJkImAASIjh2lkyc9l2nbVrrjDumNN9yXee456Z573O/yU+bgQXPLSgCoDYRMAAgQnTpVPZIpSb/4hfTqq5LT6Xru3Xelf/3LDJpVOXSIkAmg9hAyASBAdOpkTnNX5ZZbzLfCP/yw4vFTp6THHpPeekuKiPBcx8WL5pqbLMQOoLYQMgEgQMTGVj1dXmbKFOnnP5cWL5Zyc6WSEnOK/JlnpJtuqvr6w4elhAQpiL8FANQSfrwAQICIjfVuJFOSxo6VVqyQkpOluDjpttuk5s2lxx/37vqDB6WuXavbUwCoWgN/dwAAYIqJkQoLzalsbxZIHzTI/GRkSH//u/SjH3leF/NKBw4QMgHULkYyASBAOBzeP5d5pebNpUcekaKjvb9m/36pWzff2gEAXxAyASCAxMVJx4/XfjuETAC1jZAJAAEkIcF8Kac2FRebo6UJCbXbDoD6jZAJAAGke3fpyy9rt42jR81p+YYNa7cdAPUbIRMAAkivXtIXX9RuG/v2ST161G4bAEDIBIAA0q2b+ea31W4+dtm3zxwxBYDaRMgEgADSuLHUpo2Umlp7bXz5JSETQO0jZAJAgOnVS9q7t/bq//JLqWfP2qsfACRCJgAEnN69pT17aqfuS5ekr7+W4uNrp34AKEPIBIAA06ePtHt37dR98KB0/fVScHDt1A8AZQiZABBgajNkfvEFU+UArg5CJgAEmHbtzP3LMzPtr3v7dqlfP/vrBYDKCJkAEGAcDunGG6Vdu+yve+tWafBg++sFgMoImQAQgG66Sdq50946MzKks2dZvgjA1UHIBIAAVBsh89//lgYNkoL4yQ/gKuBHDQAEoJtukv77X3vr3LJFuu02e+sEAHcImQAQgOLjzRd/srPtq3PzZmnIEPvqAwBPCJkAEIAcDnunzLOypFOnzN2EAOBqIGQCQIDq21fascOeurZskW69lUXYAVw9hEwACFA332xfyPzkE+nb37anLgDwBiETAALUgAHStm2S01nzuj75RBo6tOb1AIC3CJkAEKDatjU/n31Ws3rOnzfXx2Q7SQBXEyETAALYD38orV1bszo++cR8q5z1MQFcTfzIAYAAVhYyDaP6dfzrXzyPCeDqI2QCQADr3t0MmPv3V+96p1PauFG6/XZ7+wUAVSFkAkAAczikO++U1qyp3vUffyy1aCFdf729/QKAqhAyASDA3XWX9M471bt28WJpwgR7+wMA3iBkAkCAS0yUcnOlQ4d8uy4tTdq6VRo3rnb6BQCeEDIBIMA5HNUbzfzjH6X77pMaN66dfgGAJ4RMALgGjBsnrV7tffniYun115kqB+A/hEwAuAb07Svl50tffuld+X/8Q+raVerSpXb7BQDuEDIB4BrgcEiTJpkfb9bMXLRIeuyx2u8XALhDyASAa8Rjj0mFhdKf/uS53O7dUmqq9P3vX5VuAYAlQiYAXCOCg6Vly6Tp06Wvv7Yuc+aMua7mvHlSgwZXt38AcCVCJgBcQ7p0kZ58UvrpT12nzbOzpe9+V/r5z1m2CID/ETIB4BozebKUkyP9/vfSyZNm2CwsNKfHR440QygA+BshEwCuMcHB0l//Kn3+uTR0qBQdbb5JfsMN0qxZ/u4dAJh4YgcArkFxcdK775r/nJcnHT8ude9uvoUOAIGAkAkA17gmTaRevfzdCwCoiOlyAAAA2I6QCQAAANsRMgEAAGA7QiYAAABsR8gEAACA7QiZAAAAsB0hEwAAALYjZAIAAMB2hEwAAADYjpAJAAAA29WbkLlt2zaNHDlSM2fO9FiupKRE8+fPV+/evRUbG6s2bdpo/PjxSktLq3Ef8vLyFBcXpyFDhnh9zcKFC+VwOLR58+Yatw8AAHC11PmQ+dFHH2nYsGEaPHiw1q9fL8Mw3JYtKirSqFGjtGLFCq1Zs0YnTpxQcnKydu7cqb59++rw4cM16sukSZN0/Phxr8sfOXJEU6dOrVGbAAAA/lDnQ2Z8fLzWr1+voUOHVll2+vTp2rRpk1asWKH4+HhJUlxcnNauXauMjAyNGzdOJSUl1erHunXrtHr1aq/Ll5aW6v7771dQUJ2/RQAAoA6q8wkmLi5OwcHBuvnmmz2Wy8nJ0aJFi9SxY0f16NGjwrnY2FiNGTNGu3fv1qpVq3zuQ2ZmpiZOnKhZs2Z5fc2cOXMUHx+vxMREn9sDAADwtzofMss0atTI4/mUlBRdvHhRbdu2tTx/xx13SFK1QuaECRM0ceJEl/Dqzp49e7Rs2TItXLjQ57YAAAACQb0JmQ6Hw+P5rKwsSeaIppVOnTpJknbs2OFTuytXrlR6eromTZrkVfnLly/r/vvv19KlSxUVFeVTWwAAAIGigb87ECjatWsnSTp06JByc3PVtGnTCufLXhjKyMjwus60tDQ9++yz2rJli9fPVs6YMUNDhw4tHzkFAAC4FtWbkcyqDBgwQK1bt1ZJSYmWLl3qcv706dOSpJCQEK/rHD9+vGbOnFk+ClqV5ORkrVu3TrNnz/a6jSsVFRUpNze3/AMAAOAvAR8yn3nmGTkcDp8+DzzwgM/thIaGatmyZQoJCdGMGTO0fPlyFRcXKy8vT2+99Zbmzp0rSV4HxiVLligsLExJSUlelS8oKNDDDz+sv/zlLwoLC/O5/5I0e/ZsRUZGKjIy0u2zpQAAAFdDwIfMOXPmyDAMnz7Lly+vVlsjRoxQSkqKRowYoRkzZqh///565JFH1LRp0/Lp8gEDBlRZT2pqql544QW9/vrrXrc9efJk3XvvvTV6m3zatGnKyclRTk6OLYvHAwAAVBfPZFaSmJiotWvXVji2a9cupaamSpLGjRvn8Xqn06mkpCS99NJLat68uVdtfvjhh9qzZ48WLVpUvU5/IzQ0VKGhoZKk4ODgGtUFAABQE4RML7z44ouSpF69emn48OEeyyYnJys5OVmjR492W2bLli3lb7sbhqHnn39en376qRo0cH87yhaT//Wvf63f/OY3Pn4DAACAq4uQWYXt27dr5cqVCgoK0uLFi6tcCik0NFRdunSxPFdYWKhTp04pLCxMHTt2LD/erl07t9d89dVXunjxojp06KDGjRt7PToKAADgT4RMD3JycpSUlFQ+2jhw4MAqr+nXr58OHjxoeW7z5s0aOnSo+vXrp82bN5cff+ONN9zWN2TIEG3ZskVvvPGGhgwZ4utXAAAA8IuAf/HHLmV7jpeWlnpVPisrSyNGjNDRo0c1a9YsTZ482bLc1KlTNWjQIO3bt8+2vgIAAFzr6kXIdDqd5Tv17NixQ06n023ZzMxMLVy4UD179tSJEyf0/vvva9q0aZZlMzIyNG/ePCUnJ2vJkiW10ncAAIBrUZ0PmU8//bSio6O1ceNGSdKGDRsUHR2t6dOnVyhXWFioli1bKiEhQatXr9aUKVN05MgRjRw50m3dzZo10+jRoxUTE6OxY8fW6vcAAAC4ljiMsgUgUacUFBQoIiJCkpSfn6/w8HA/9wgAAFwtgZAD6vxIJgAAAK4+QiYAAABsR8gEAACA7QiZAAAAsB0hEwAAALYjZAIAAMB2hEwAAADYjpAJAAAA2xEyAQAAYDtCJgAAAGxHyAQAAIDtCJkAAACwHSETAAAAtiNkAgAAwHaETAAAANiOkAkAAADbETIBAABgO0ImAAAAbEfIBAAAgO0ImQAAALAdIRMAAAC2I2QCAADAdoRMAAAA2I6QCQAAANsRMgEAAGA7QiYAAABsR8gEAACA7QiZAAAAsB0hEwAAALYjZAIAAMB2hEwAAADYjpAJAAAA2xEyAQAAYDtCJgAAAGxHyAQAAIDtCJkAAACwHSETAAAAtiNkAgAAwHaETAAAANiOkAkAAADbETIBAABgO0ImAAAAbEfIBAAAgO0ImQAAALAdIRMAAAC2I2QCAADAdoRMAAAA2I6QCQAAANsRMgEAAGA7QiYAAABsR8gEAACA7QiZAAAAsB0hEwAAALYjZAIAAMB2hEwAAADYjpAJAAAA2xEyAQAAYDtCJgAAAGxHyAQAAIDtCJkAAACwHSETAAAAtiNkAgAAwHaETAAAANiOkAkAAADbETIBAABgO0ImAAAAbEfIBAAAgO0ImQAAALAdIRMAAAC2I2QCAADAdoRMAAAA2I6QCQAAANsRMgEAAGA7QiYAAABsR8gEAACA7QiZAAAAsB0hEwAAALarNyFz27ZtGjlypGbOnOmxXElJiebPn6/evXsrNjZWbdq00fjx45WWllbjPuTl5SkuLk5DhgzxWO6ll16Sw+Fw+UydOrXGfQAAALga6nzI/OijjzRs2DANHjxY69evl2EYbssWFRVp1KhRWrFihdasWaMTJ04oOTlZO3fuVN++fXX48OEa9WXSpEk6fvy4xzKXL1/W/PnzXY43bNhQjz/+eI3aBwAAuFrqfMiMj4/X+vXrNXTo0CrLTp8+XZs2bdKKFSsUHx8vSYqLi9PatWuVkZGhcePGqaSkpFr9WLdunVavXl1luTfeeEO33nqrDhw4UOFz6NAhtW/fvlptAwAAXG11PmTGxcUpODhYN998s8dyOTk5WrRokTp27KgePXpUOBcbG6sxY8Zo9+7dWrVqlc99yMzM1MSJEzVr1iyP5ZxOp1544QX96le/0g033FDh07lzZ5/bBQAA8Jc6HzLLNGrUyOP5lJQUXbx4UW3btrU8f8cdd0hStULmhAkTNHHiRJfwWtk777yjnJwcffbZZzp58qTP7QAAAASKehMyHQ6Hx/NZWVmSzBFNK506dZIk7dixw6d2V65cqfT0dE2aNKnKsnPmzNHZs2f14IMPqnPnzrr99tv12Wef+dQeAABAIGjg7w4Einbt2kmSDh06pNzcXDVt2rTC+bIXhjIyMryuMy0tTc8++6y2bNmioCDPef7AgQMKDQ1VfHy8jh07JsMw9PHHH2vAgAGaN2+eJk+e7NP3ufIFp4KCAp+uBQAA17Yr/+739NJzbSJkfmPAgAFq3bq1zp49q6VLl+rpp5+ucP706dOSpJCQEK/rHD9+vGbOnFk+CupJ165d9emnn0qS0tPT9eabb2rmzJnKycnRU089pejoaD300EMe6ygqKlJRUZEk6fz58+XHW7Vq5XWfAQBA3VJYWKiIiIir3m7AT5c/88wzlmtGevo88MADPrcTGhqqZcuWKSQkRDNmzNDy5ctVXFysvLw8vfXWW5o7d64keRUYJWnJkiUKCwtTUlKSz31p2bKlJk2apP3796tLly6SpKeeekp5eXker5s9e7YiIyMVGRmphIQEn9sFAAB1T9kA1NUW8COZc+bM0Zw5c65KWyNGjFBKSoqee+45zZgxQwsWLFCXLl103333lQ81DxgwoMp6UlNT9cILL5SPTFZX27ZttWnTJnXr1k0XLlzQhg0bdNddd7ktP23aND355JOSzDfVT5w4oRtvvFGnT59WZGRkjfoCe+Xm5qpt27ZKS0tzeTQD/sN9CVzcm8DFvQlMhmHo3LlzSkhIUJMmTfzSh4APmVdbYmKi1q5dW+HYrl27lJqaKkkaN26cx+udTqeSkpL00ksvqXnz5jXuT/v27fXEE0/o97//fXkf3AkNDVVoaGj5v8fFxUmSIiIiFB4eXuO+wD6lpaWSpPDwcO5NAOG+BC7uTeDi3gQup9MpSVW+F1JbAn66PBC8+OKLkqRevXpp+PDhHssmJycrOTlZo0ePdpnGL1sQfsuWLeXHvFG2fJI/nqcAAACoDkYyq7B9+3atXLlSQUFBWrx4cZXBMDQ0tPw5ysoKCwt16tQphYWFqWPHjl73oU2bNpKk/v37e99xAAAAPyJkepCTk6OkpCQZhqHnn39eAwcOrPKafv366eDBg5bnNm/erKFDh6pfv37avHmz1/348ssvNXDgQCUmJnp9jWQG3l//+tcVptARGLg3gYn7Eri4N4GLexO4/H1v6s10edme42XPjlQlKytLI0aM0NGjRzVr1iy361ROnTpVgwYN0r59+2rUv+LiYuXm5loeX7x4sf74xz/6XGdoaKh+85vf8Ac/AHFvAhP3JXBxbwIX9yZw+fve1IuQ6XQ6y3fq2bFjR/mDsFYyMzO1cOFC9ezZUydOnND777+vadOmWZbNyMjQvHnzlJycrCVLltSoj4mJiWrZsqWee+45FRYWSpJOnjypxx9/XHPnzlXXrl1rVD8AAMDVVOdD5tNPP63o6Ght3LhRkrRhwwZFR0dr+vTpFcoVFhaqZcuWSkhI0OrVqzVlyhQdOXJEI0eOdFt3s2bNNHr0aMXExGjs2LE16ueMGTN03XXX6fe//706d+6sH/3oR/rHP/6h+fPn+zxNDgAA4G8Ow197DQEAAKDOqvMjmdeqy5cva86cOerSpYvi4+N12223aevWrT7VkZ+frylTpqhz584KCQlR+/bt9eijj+rMmTNur/nqq6/00EMPKS4uTm3atFH37t316quvev0sa31gx70pLS3Vq6++qu7duyssLEydOnXStGnTqtyV4W9/+5tuvvlmxcXFqVevXtV6Vreu8ud9kaS9e/fqnnvu0fjx46v7Feosf92bTz75RLfffruaNGmi8PBwDRw4UKtWrarp16lT/HVvDh48qB/+8Idq1aqVIiIiNHToUG3fvr2mX6dO8ffPtDJff/21oqOjq7WbogwEnEuXLhlDhw41unXrZpw8edIwDMNYvXq10bBhQ2P16tVe1ZGXl2fceOONhiQjODjYcDgchiRDktG6dWvj8OHDLtf897//NaKiooynnnrKKCoqMgzDMFatWmU0bNjQuPvuu43S0lL7vuQ1yo57YxiG8eCDDxrBwcFGhw4djIYNG5bfm//3//6f22umTZtmREREGJs3bzYMwzAOHDhgtGjRwnj88cdr9qXqAH/el//85z/GXXfdVf5nLCkpqaZfp07x171ZsWKFERQUZEgyGjRoUF5ekvHkk0/a8t2udf66N1988YXRpEkTIzo62mjZsmV5+UaNGhm7du2y46td8/z5M+1KTqfT+M53vlPtn22EzAD0i1/8wpBk/Oc//6lw/J577jEaN25sHDt2rMo6pkyZYvTp08f4+OOPjaKiIiMvL8+YN29e+Q/bAQMGVChfUFBgdOrUyejXr59LXc8995whyXj++edr9sXqADvuzapVq4y7777bOHPmjGEYhpGfn2889NBD5X/49+zZ43LN2rVrDUnG3LlzKxxfunSpIcl4++23a/Ctrn3+ui+GYRjHjh0zSktLjaSkJEKmBX/cm/T0dCMyMtKYMWNG+TX//e9/jb59+5Zfs2HDBpu+4bXLH/emtLTUGDRokPG3v/3NcDqdhmEYxtatW40WLVoYkowf/vCHNn27a5s/f6ZdaeHChUZERAQhs644fvy40aBBA6Nbt24u5z744ANDkjFu3DiPdZSUlBj9+/c3Lly44HJuxowZ5f+Bpaamlh8vCyu/+c1vXK65cOGC0bBhQyMqKsrIy8vz/UvVEXbcG8MwjHnz5rmMChcXFxtxcXGGJJffUktLS43rrrvOcDgcxrlz5yqcy8vLM4KDg4127doZJSUl1fhW1z5/3ZfKXnvtNUJmJf66Ny+//LKxcOFCl3rS09ONmJgYQ5Lxk5/8xMdvU7f4695s3769fDbmSn/6058MSZb9qW8C5Wfa4cOHjY4dOxqzZ8+u9s82nskMMG+//bZKSkosF36/5ZZbJElr165VZmam2zrOnj2rqVOnKioqyuXclet9nj9/vvyf//Wvf0mS2rZt63JNVFSUEhMTlZ2drQ8//NDr71LX2HFvJHPFg8r7yDZo0KB8FYFevXpVOLdjxw4dOXJE8fHxatmyZYVzERER6t69u06fPq0PPvjA5+9UF/jrvlTWqFEjX7pdL/jr3oSEhGjChAku9bRo0UJJSUmSKv78q4/8dW/69++v2267zaWesh3t+vTp4/V3qKsC4WdaaWmpkpKSNH/+fLVu3drXr1COkBlg1q1bJ0mKi4tzORcTE6N27drp8uXLSk5OdltHu3bt9IMf/MDyXGRkZHlQuXJry6ysLEnmLkdWOnXqJEnl643WR3bcG0/Onj2re++912VbUk/tSlLPnj0lmS851Ef+ui+VVbXlbH3kr3szYcIEl79cy1x33XWS/vczrb4KlD83V5YPDQ3VM888U6326pJAuDdz585VQkKC7rzzzmq1UYaQGWB27dolSWrfvr3l+bLRyd27d1er/pKSEmVnZ6tfv37le6JLZjCV3IdI45uVrjIyMqrVbl1Qm/dm586d5bs7Xc126wJ/3RdULRDvTdnPsDFjxvjcZl0SaPfmtdde05/+9KfyX5rrM3/fmz179ujPf/6zFixY4HP9lREyA8ilS5eUn58vSZZT3ZI5EilVP+z9+9//1uXLl/X0009XOF62mPw//vEPffXVVy7XnT59WpI5DVUf1ea9+fDDDzVq1Chdf/31KigocDlfNq1XW/9NXMv8eV/gWaDem02bNqlnz54aPny4T9fVJYF0by5cuKAHH3xQycnJPHIi/9+by5cvKykpSa+//np5OzVByAwgVz5f0bhxY8syZVNAly5dqlYbCxYs0B133KG77rqrwvHRo0frJz/5iS5duqTvfe975XuxnzhxQlOnTi0f4ayvU0y1cW/279+ve+65R9///vd19uxZvfHGG7rpppt04MABy7Zr67+Ja5k/7ws8C8R7s2fPHm3btk2LFi1yO51eHwTKvXn55ZfVt29fLV++XGfPntVdd92lX/7yl95/kTrI3/fmV7/6lb797W9r6NCh1ei9RV9tqQW2uHKU0HCzEdPly5clmc9l+Grz5s3atm2bli9fbnn+L3/5i1577TU1atRIw4cP17e//W0tXrxYXbt2LW93wIABPrdbF9TGvenWrZveeustpaen67nnnlOjRo109uxZPfzww5Zt18Z/E9c6f94XeBaI92bSpEmaOnWqBg8e7FX5uipQ7s0vf/lLHT16VFu3bi3/u+WVV17RRx995O1XqXP8eW9SUlL0wQcfaNasWdXsvStCZgCJiYkp/w/M3VB2dna2JKl58+Y+1X3hwgU99thjWrNmTfnzl5U5HA5NmDBBn332mb7++mt9/PHHmjt3bvnzIW3btq23P5xr895ER0fr//7v//TOO+9IMv+gX/nIQtmbfXa3Wxf4877As0C7N6+88ooiIiL0u9/9zqe26qJAujcOh0ODBw/Wli1byt8wf+utt3xqsy7x170pKCjQww8/rOXLl9v62AIhM4AEBwerW7dukqS0tDTLMufOnZPk2zIPpaWluv/++/W73/1OgwYN8qlPGRkZeuONNySZyx/V1ymm2ro3V/re976nfv36ubRRtsxEbbV7LfPnfYFngXRvtmzZorVr12rVqlUKDg6uVlt1SSDdmzINGzbUr371K6/L11X+ujdr1qzRgQMHlJiYKIfDUeHz4IMPSjJnOx0Oh2JjY71uq34mhgBW9jB62TORV8rIyFBOTo7Cw8N9GlGcMGGCxowZU62lCKZPn67s7Gz17NlTjz/+uM/X1yW1cW8qK/sl4Mo3/z21K0lHjx6VJI0YMaLa7V7L/HVfULVAuDf79u3TjBkztHbtWrfPuNVHgXBvalq+rvLHvYmIiFCXLl0sP2WzaU2bNi3fR91bhMwAM378eAUFBWnr1q0u57Zv3y5JuvPOOxUaGupVfZMnT9Z1111n+VxMZmamcnNz3V67bt06vf7664qJidHf//53NWzY0MtvUTfZfW+s5OTkqHfv3hVesLrjjjvUuXNnHThwwGUB6ezsbB04cECdO3cun2qqb/x1X1A1f9+bw4cPa8KECVq9erWio6Ndzh8/frza7V7r/H1v3JWXWF7KH/fmhz/8oQ4ePGj5mT17doUyZZu3eMXnPYJQ6x599FFDkrFr164Kx++8804jLCyswnaQH3/8sdGvXz/jlVdecannqaeeMn77299atrF3715j8ODBRn5+vuX5jz76yAgPDzdatWrl0o/6zK57YyUzM9No06aNsWnTJpdzq1atMiQZL730UoXjCxYsMCQZK1eu9Pm71CX+ui9X+sMf/mBIMn784x/73P+6zF/35tChQ8att95qnDx50uVcYWGh8eKLLxozZ8707cvUMYHw5+ZKv//9740hQ4aU72lenwXSvfnzn//M3uV1SX5+vpGYmGjccsstRmZmpuF0Oo1XX33VCAkJMd55550KZUeNGmVIMiIiIsqPOZ1O47HHHjMcDofRrFmzCp+YmBgjLCzMkGTcd999Lm3v2rXLePTRR43g4GDju9/9rpGWllbr3/daUtN7c/78eSMuLs7o0aOHsWzZMuPSpUuGYRjG0aNHjdtvv91jWHzkkUeMZs2aGXv27DEMwzC2bt1qNG3a1Jg0aVItfNNriz/vS5mHH37YkGT07NnTuHjxor1f8Brmj3uzZ88eo1WrVkZ4eLjLz8CoqCgjKCjIkGQcOXKkdr98gPPHvZk9e7bRrFkz44EHHjAOHDhgGIa5n/Zrr71mDBs2zMjKyqrFb3ztCISfaWUImXVQbm6u8Ytf/MLo3LmzER8fb4wZM6Y8XFzpr3/9q9GkSRPj5z//efmxKVOmGJKq/HzwwQfl1yxfvtxo0qSJ0b59e2PcuHHGhg0brsr3vBbV5N4UFxcbDz74oNGsWTMjKCjIaNGihTFs2DDjqaeeMr766iuP7TqdTmPBggVG165djbi4OGPAgAHGu+++a/v3u1b56768/PLLRkxMTIU/W+Hh4cb9999v+3e8Vl3Ne3P69GkjOjq6yp9//fr1q9XvfK242n9uPv74Y6NHjx5GaGioERISYvTu3dsYN26csWrVKkYwK/HXz7TKahIyHYbhZiEmAAAAoJp48QcAAAC2I2QCAADAdoRMAAAA2I6QCQAAANsRMgEAAGA7QiYAAABsR8gEAACA7QiZAAAAsB0hEwAAALYjZAIAAMB2hEwAAADYjpAJAAAA2xEyASAAZWVlae7cuRoyZIjatGmj0NBQNW/eXImJiZoyZYoOHz7s7y4CgEcOwzAMf3cCAPA/f/rTn/Tkk08qNzdXkuRwOBQVFaX8/HwVFxdLkoKDgzVx4kS98MILatCggT+7CwCWGMkEgADy29/+Vg8//LByc3M1aNAgrV+/XhcvXlRWVpaKior0+eef66GHHpLT6dQrr7yi733veyopKfF3twHABSOZABAg/vnPf2rMmDEyDEM///nP9eqrryooyHosYPXq1brnnnvkdDr11FNP6fnnn7/KvQUAzwiZABAASktL1b17dx06dEi33nqrtm7d6jZglvm///s/zZo1Sw0aNNChQ4cUFxd3lXoLAFVjuhwAAsAnn3yiQ4cOSZJ+85vfVBkwJWnKlCkKDw9XSUmJ/vjHP9Z2FwHAJ4RMAAgA//rXvyRJERERGjJkiFfXREZGavDgwRWuB4BAQcgEgACQmpoqSUpISPDpbfEuXbpIko4fP14r/QKA6iJkAkAAyM7OlmSOZPqirHxOTo7dXQKAGiFkAkAAiImJkSRduHDBp+vKyjdv3rzC8dLSUs2dO1cJCQkKDQ1VfHy8nnvuOZY7AnDVEDIBIAB06NBBknT48GFdunTJ6+v27t0rSWrfvn2F448//rieeeYZJSYmatGiRfrOd76jX/3qV3rkkUfs6zQAeEDIBIAA8N3vfleSVFxcrPXr13t1TXp6urZv3y5JGj58ePnxL774QkuWLNG4ceP09ttv6+GHH9aSJUv09NNPa9myZfr888/t/wIAUAkhEwACwNChQ8tHM71dWP2ll15SaWmpHA6H7rnnnvLjq1atkmEYeuKJJyqUL/v3VatWVTg+fvx4NW7cWKdOnVJSUpKaNWum6OhoPfrooyopKVF+fr4mT56sDh06qEmTJvrBD36gzMzMmnxdAPUAIRMAAkBQUJDmzp0rSdq+fbvmzJnjsfy///1vvfjii5Kkhx56SF27di0/9/nnnysoKEh9+/atcE27du3Url07l5HM3bt3KzIyUt/61rcUERGhWbNmKTExUUuXLtXcuXN166236tSpU3r22Wd111136b333tOvf/1rO742gDqMHX8AIICMHz9ey5YtU1BQkNasWaMxY8a4lElNTdXAgQOVnp6uHj16KCUlRU2aNCk/37NnT6Wnp+vcuXMu1/br108XLlzQkSNHJEklJSWKiIhQaWmpNm3apNtuu02SlJubq+joaDmdTi1cuFA///nPy+vo0qWLmjRpwrQ7AI8YyQSAALJkyRKNHTtWTqdTY8eO1dSpU1VcXFx+/s0339RNN92k9PR0denSRR9++GGFgClJhYWFCg0Ntay/UaNGunjxYvm/HzhwQEVFRfrpT39aHjAlqUmTJmrYsKH69+9fIWBKUtOmTRUcHGzH1wVQhxEyASAAtG7dWq1bt1aHDh20detWSZLT6dS8efP01ltvSZK++uor/fjHP1Zubq4k6cyZM0pMTCy/NiUlRZIZJIuKiizbuXTpkho1alT+77t375Yk3X333RXKHTlyREVFRbrzzjsrHDcMQ4cPH9YNN9xQ8y8NoE7zflsJAECtsZraLuN0Oiv8b5nc3NzywClJly9flmQuZ3Tw4EFdvnxZISEhFa5JS0tTfHx8+b/v3r1bDofD5fnNXbt2STKn16907Ngx5ebm6sYbb/T2qwGopwiZABAAvHk8PjY21qtyiYmJ2rhxoz7//HMNHDiw/Pjp06d1+vRpjRs3rvzY7t27lZCQ4DLlXhY++/Tp43JckstxAKiM6XIAqGPGjRsnh8OhV199tcLxsn+/MmTu2bPHclRy9+7dio+PV9OmTV2OS4RMAFVjJBMA6pjevXvrZz/7mZYuXSrDMDRs2DB9/vnnWrp0qZKSksqnwE+dOqXMzEzLkLlr1y5961vfsjzeqVMnRUVF1fbXAHCNI2QCQB20cOFCderUSX/84x/17rvvqm3btvrNb36jadOmlZfZs2ePJNdRyXPnzuncuXO66aabXOrdvXu3y/ObAGCFdTIBAABgO57JBAAAgO0ImQAAALAdIRMAAAC2I2QCAADAdoRMAAAA2I6QCQAAANsRMgEAAGA7QiYAAABsR8gEAACA7QiZAAAAsB0hEwAAALYjZAIAAMB2/x/Ufzi5RBiMfgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "\n",
    "data = np.load('contour_data_wcdm_21.npz')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "OM_grid7 = data['OM_grid7']\n",
    "M_grid7 = data['M_grid7']\n",
    "Z_7 = data['Z_7']\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "levels7 = [\n",
    "    chi2_wwcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wwcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wwcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "contour7 = ax.contour(OM_grid7, M_grid7, Z_7, levels=levels7, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "ax.plot(omwwcdm, Mwwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r'M')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1d4bcdcc-157c-49d3-8fcf-db675c54951c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.ticker import MaxNLocator, FormatStrFormatter\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "# Φόρτωση δεδομένων\n",
    "data = np.load('contour_data_wcdm_21.npz')\n",
    "\n",
    "# Δεδομένα για το 1ο γράφημα (Ω_0m vs h)\n",
    "\n",
    "\n",
    "OM_grid8 = data['OM_grid8']\n",
    "h_grid8 = data['h_grid8']\n",
    "Z_8 = data['Z_8']\n",
    "\n",
    "# Δεδομένα για το 2ο γράφημα (M vs h)\n",
    "\n",
    "\n",
    "M_grid9 = data['M_grid9']\n",
    "h_grid9 = data['h_grid9']\n",
    "Z_9 = data['Z_9']\n",
    "\n",
    "# Επίπεδα (Σημείωση: τα chi2_wcdm, chi2_wwcdm και dchi πρέπει να έχουν οριστεί προηγουμένως)\n",
    "\n",
    "levels_wwcdm = [\n",
    "    chi2_wwcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wwcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wwcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "# Δημιουργία σχήματος με 2 υπογραφήματα\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 6), sharey=True)\n",
    "\n",
    "# --- 1ο Γράφημα (Ω_0m vs h) ---\n",
    "\n",
    "contour8 = ax1.contour(OM_grid8, h_grid8, Z_8, levels=levels_wwcdm, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "ax1.plot(omwwcdm, hwwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax1.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax1.set_ylabel(r'h')\n",
    "\n",
    "# Περιορισμός σε 2 δεκαδικά ψηφία και αφαίρεση του τελευταίου αριθμού δεξιά\n",
    "ax1.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax1.xaxis.set_major_locator(MaxNLocator(prune='upper', nbins=5))\n",
    "\n",
    "# --- 2ο Γράφημα (M vs h) ---\n",
    "\n",
    "contour9 = ax2.contour(M_grid9, h_grid9, Z_9, levels=levels_wwcdm, colors='blue', linewidths=0.7)\n",
    "\n",
    " \n",
    "ax2.plot(Mwwcdm, hwwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax2.set_xlabel(r'M') \n",
    "\n",
    "# Περιορισμός σε 2 δεκαδικά ψηφία και αφαίρεση του πρώτου αριθμού αριστερά\n",
    "ax2.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax2.xaxis.set_major_locator(MaxNLocator(prune='lower', nbins=5))\n",
    "\n",
    "# Μορφοποίηση πλαισίων (spines) και για τα δύο γραφήματα\n",
    "for ax in (ax1, ax2):\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_edgecolor('black')\n",
    "        spine.set_linewidth(1.5)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Κρύψιμο των αριθμών του άξονα y στο δεύτερο γράφημα\n",
    "ax2.tick_params(labelleft=False)\n",
    "ax2.set_xlim(-19.45, -19.38)\n",
    "# Ένωση των γραφημάτων χωρίς κενό\n",
    "plt.subplots_adjust(wspace=0)\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6c71057e-6e5b-4bdc-88a0-1e0cffca73a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.ticker import MaxNLocator, FormatStrFormatter\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "# Φόρτωση δεδομένων\n",
    "data = np.load('contour_data_wcdm_21.npz')\n",
    "\n",
    "# Δεδομένα 1ου γραφήματος (Ω_0m vs w_0)\n",
    "\n",
    "\n",
    "OM_grid10 = data['OM_grid10']\n",
    "wo_grid10 = data['wo_grid10']\n",
    "Z_10 = data['Z_10']\n",
    "\n",
    "# Δεδομένα 2ου γραφήματος (M vs w_0)\n",
    "\n",
    "\n",
    "M_grid11 = data['M_grid11']\n",
    "wo_grid11 = data['wo_grid11']\n",
    "Z_11 = data['Z_11']\n",
    "\n",
    "# Δεδομένα 3ου γραφήματος (h vs w_0)\n",
    "\n",
    "h_grid12 = data['h_grid12']\n",
    "wo_grid12 = data['wo_grid12']\n",
    "Z_12 = data['Z_12']\n",
    "\n",
    "# Επίπεδα (τα chi2_wcdm, chi2_wwcdm και dchi πρέπει να έχουν οριστεί προηγουμένως)\n",
    "\n",
    "\n",
    "levels_wwcdm = [\n",
    "    chi2_wwcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wwcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wwcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "# Δημιουργία σχήματος με 3 υπογραφήματα\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(16, 6), sharey=True)\n",
    "\n",
    "# --- 1ο Γράφημα (Ω_0m vs w_0) ---\n",
    "\n",
    "contour10 = ax1.contour(OM_grid10, wo_grid10, Z_10, levels=levels_wwcdm, colors='blue', linewidths=0.7)\n",
    "\n",
    " \n",
    "ax1.plot(omwwcdm, wowwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax1.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax1.set_ylabel(r'$w_{0}$')\n",
    "\n",
    "ax1.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax1.xaxis.set_major_locator(MaxNLocator(prune='upper', nbins=5))\n",
    "\n",
    "# --- 2ο Γράφημα (M vs w_0) ---\n",
    "\n",
    "contour11 = ax2.contour(M_grid11, wo_grid11, Z_11, levels=levels_wwcdm, colors='blue', linewidths=0.7)\n",
    "\n",
    "\n",
    "ax2.plot(Mwwcdm, wowwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax2.set_xlabel(r'M') \n",
    "\n",
    "ax2.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax2.xaxis.set_major_locator(MaxNLocator(prune='upper', nbins=5))\n",
    "\n",
    "# --- 3ο Γράφημα (h vs w_0) ---\n",
    "\n",
    "contour12 = ax3.contour(h_grid12, wo_grid12, Z_12, levels=levels_wwcdm, colors='blue', linewidths=0.7)\n",
    "\n",
    " \n",
    "ax3.plot(hwwcdm, wowwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax3.set_xlabel(r'h') \n",
    "\n",
    "ax3.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax3.xaxis.set_major_locator(MaxNLocator(nbins=5)) # Εδώ δεν χρειάζεται prune γιατί είναι το τελευταίο δεξιά\n",
    "\n",
    "# Μορφοποίηση πλαισίων (spines) και για τα τρία γραφήματα\n",
    "for ax in (ax1, ax2, ax3):\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_edgecolor('black')\n",
    "        spine.set_linewidth(1.5)\n",
    "\n",
    "\n",
    "ax.set_ylim(-1.2, -0.9)\n",
    "ax2.set_xlim(-19.45, -19.38)\n",
    "# Κρύψιμο των αριθμών του άξονα y στο δεύτερο και τρίτο γράφημα\n",
    "ax2.tick_params(labelleft=False)\n",
    "ax3.tick_params(labelleft=False)\n",
    "\n",
    "# Ένωση των γραφημάτων χωρίς κενό\n",
    "plt.subplots_adjust(wspace=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e41211a9-02a9-4723-9132-486115d7a9e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Όλα τα δεδομένα φορτώθηκαν με επιτυχία!\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 1. Φόρτωση του αρχείου\n",
    "data = np.load('contour_data_wcdm_21.npz')\n",
    "\n",
    "# 2. Επαναφορά των μεταβλητών για το κάθε contour ξεχωριστά\n",
    "\n",
    "# Contour 1\n",
    "OM_grid1 = data['OM_grid1']\n",
    "M_grid1 = data['M_grid1']\n",
    "Z_1 = data['Z_1']\n",
    "\n",
    "# Contour 2\n",
    "OM_grid2 = data['OM_grid2']\n",
    "h_grid2 = data['h_grid2']\n",
    "Z_2 = data['Z_2']\n",
    "\n",
    "# Contour 3\n",
    "M_grid3 = data['M_grid3']\n",
    "h_grid3 = data['h_grid3']\n",
    "Z_3 = data['Z_3']\n",
    "\n",
    "# Contour 4\n",
    "OM_grid4 = data['OM_grid4']\n",
    "wo_grid4 = data['wo_grid4']\n",
    "Z_4 = data['Z_4']\n",
    "\n",
    "# Contour 5\n",
    "M_grid5 = data['M_grid5']\n",
    "wo_grid5 = data['wo_grid5']\n",
    "Z_5 = data['Z_5']\n",
    "\n",
    "# Contour 6\n",
    "h_grid6 = data['h_grid6']\n",
    "wo_grid6 = data['wo_grid6']\n",
    "Z_6 = data['Z_6']\n",
    "\n",
    "# Contour 7\n",
    "OM_grid7 = data['OM_grid7']\n",
    "M_grid7 = data['M_grid7']\n",
    "Z_7 = data['Z_7']\n",
    "\n",
    "# Contour 8\n",
    "OM_grid8 = data['OM_grid8']\n",
    "h_grid8 = data['h_grid8']\n",
    "Z_8 = data['Z_8']\n",
    "\n",
    "# Contour 9\n",
    "M_grid9 = data['M_grid9']\n",
    "h_grid9 = data['h_grid9']\n",
    "Z_9 = data['Z_9']\n",
    "\n",
    "# Contour 10\n",
    "OM_grid10 = data['OM_grid10']\n",
    "wo_grid10 = data['wo_grid10']\n",
    "Z_10 = data['Z_10']\n",
    "\n",
    "# Contour 11\n",
    "M_grid11 = data['M_grid11']\n",
    "wo_grid11 = data['wo_grid11']\n",
    "Z_11 = data['Z_11']\n",
    "\n",
    "# Contour 12\n",
    "h_grid12 = data['h_grid12']\n",
    "wo_grid12 = data['wo_grid12']\n",
    "Z_12 = data['Z_12']\n",
    "\n",
    "print(\"Όλα τα δεδομένα φορτώθηκαν με επιτυχία!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0075bb14-6dd1-4760-81d8-8be57ec7cd8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "\n",
    "data = np.load('contour_data_wcdm_21.npz')\n",
    "\n",
    "\n",
    "OM_grid1 = data['OM_grid1']\n",
    "M_grid1 = data['M_grid1']\n",
    "Z_1 = data['Z_1']\n",
    "\n",
    "\n",
    "OM_grid7 = data['OM_grid7']\n",
    "M_grid7 = data['M_grid7']\n",
    "Z_7 = data['Z_7']\n",
    "\n",
    "\n",
    "levels1 = [\n",
    "    chi2_wcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "levels7 = [\n",
    "    chi2_wwcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wwcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wwcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "\n",
    "contour1 = ax.contour(OM_grid1, M_grid1, Z_1, levels=levels1, colors='red', linewidths=0.7)\n",
    "\n",
    "\n",
    "contour7 = ax.contour(OM_grid7, M_grid7, Z_7, levels=levels7, colors='blue', linewidths=0.7)\n",
    "\n",
    "ax.plot(omwcdm, Mwcdm, 'ko', markersize=8) \n",
    "ax.plot(omwwcdm, Mwwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r'M')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "\n",
    "custom_lines = [Line2D([0], [0], color='red', lw=2),\n",
    "                Line2D([0], [0], color='blue', lw=2)]\n",
    "ax.legend(custom_lines, ['Pantheon+CMB+BAO+Riess', 'Pantheon+CMB+BAO'], loc='best', fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "393dbe22-e946-44d2-951e-87e502d1ef1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I6D/37wN372r7KUrFkydP0L9/f8TExCA2NhYeHh6YM2cOGjdujCdPnuDKlSsYNmwYAKBixYrw8vLC1atXldc7ODgAAPz8/PDWW28BAKpVqwYHBwecOnWqUM9gb2+P3r17AwDMzc1Rv359lZbF+fPnw8fHB40bNwYAODk5ISgoCHPnzsWePXvQqlUrfPDBB+jVq5dy3GLFihXx9ddfo2vXrvjkk0+wdOlSled9vlvW398fERERePjwIRQKBbp3767yfPfv3weAXK1Z69evV7aUli1btlB1zavugwcPxvz58zFv3jxli+a+ffsAAC1atMDevXvzLWPz5s24fPkyjIyMcOPGDTx8+BAWFhaYM2cO3njjDeV5ffr0wd3nfl+npqYiOjoazZo1UylvypQpaNu2bZHLf15YWBhq1qyJypUrAwBGjhyJnTt3Yv78+ejQoYPKuTljVS0sLPKtZ85Y15wW8uLW588//4S/vz9OnjyJrKws/PDDD3jvvfdgZWWlcl3NmjXRtm1bhISEqHw/efJkjB49WjmmtmXLlqhTpw4WLFiAzz77DNeuXcOjR4/w9OlTODg4wN3dHSNHjlRef/r0aTg7OwOQu/jMmDFD462z2sSQSUSUnwoVtP0EBdPQM9ra2mLlypV5HrOxscGhQ4fg4+ODzMxM7Nq1C7GxsTA0NFSeo26coIWFBZ49e1asZzI3N8fTp0+VP9+zZw/u3LmjEh4SEhLg5uaGu3fv4vjx47h27RqqV6+uUk6XLl1gZWWFrVu3KkNmXs+bE26ePXsGNzc3HDhwQOV4TrgcMGBArmtzundTUlKKWk2ljz76CIsXL8aPP/6IadOmwc7ODt988w0mT55cqOu7deuGNWvWAJDjOI8cOYLx48dj5MiRWLt2LcLDw2FiYoK1a9eqXBcdHY2ZM2fmCs/FLf95ISEhmDVrlvLnbdu2RZUqVbBnzx6cP39eZW3UMmXKAECB4yFzfj/l/EOhuPVp0qQJNm3ahI8//hhfffUV/vrrL+U/pF6UM+nq+Wc4dOgQHj9+jA0bNii/T0pKgpOTE27duoWaNWvC2dkZ9evXx9SpU/HBBx/gk08+UZ7bqVMnDB8+HHfu3MHMmTPRoEEDjB07Nt9n1icMmURE+fnnH20/gc6oW7cuQkJC8Pfff2PgwIFwc3NTtt4VRBRzXUOFQqEyC/3hw4do3bo1lixZkuf5OX/Z57XeZuXKlXHp0qVC3bc4z1unTh1s3bpVOZa1OFxdXREUFIRVq1Zh8eLF6NChA27cuIFu3boVuSxDQ0M0atQIe/bsQbVq1fD3339j6dKlGDFiRLGfr6jlnzhxAmfOnMHMmTNVrs15vwsWLMBPP/2k/N7Pzw9AwZOUbt68CQAICAjQRFXw2WefYf/+/Vi3bh3efvttDB48uMBrHj16hKysLEyaNCnfme5HjhzB5MmTMWnSJMyePRvffPMNBg4cCAAYNmwYypYti0mTJsHf3x/t27fHkiVL4OTkpJF6aRsn/hARUYESExMREBCAq1evYtu2bXj33XdVWjFLi62tLf78889cS+AIIXD+/HlUqlQJAFS68XPY2NigWrVqJfZsvXv3hoGBAX7//XdkZWXle+69e/fUHps0aRIUCgXmz5+Pzz//XDlZprhsbGzQpEkTAMCFCxeKXU5xyp87dy4WLlyIAwcOqHyOHTsGS0tLrF69Go8ePVKe3759ezg6OuLQoUNITExUe8+cIQRDhgzRSB2MjIywbt062NjYYMyYMbkmm+XFxsYGCoUCmzdvznXs/v37iIuLAwCUL18eK1aswJkzZ+Dt7Y1BgwapXNO9e3dcvnwZISEhCA8PR7t27V5qsXldwpBJRETIzs7Od13AVatW4cyZM/kGnsL+xahQKIr1jADQvHlz3Lp1C4GBgbhz5w4AIC0tDZMmTcLTp0/h6+sLV1dX/Pbbb0hNTVV5tuvXryvHexbleQurevXqGDt2LKKjozF37ly15+W07uXIzs5Waa319vZGhw4d8PDhQxw+fBj9+/dXOff5/+bIry5CCGXLYN26dYtWqZco//r16zh58iT69u2b6xpHR0cMHToUz549U65NCQCWlpaYO3cunj17pnbN0Nu3b2PZsmXo2bNnoXZJykvOPwKef4/u7u744Ycf8OzZM3Tr1k0l/ObF2toafn5+CAsLw8SJE5XDJGJjYzF8+HBYW1vjn3/+we+//w5Ajuvcs2cPXF1dlcMwZsyYAQAwMTHBuHHjEBISgrNnzxZ4b33BkElE9JpLTExEbGws4uPj1a6HaWlpCUAGJEB2Z0ZFRSElJQVZWVmIiopSdp3nTI4BgPT0dCQlJeH+/fvKoJIzMSYmJgbZ2dk4cuQIsrOzERMTg8TERJXlX3L+ss1p+Rs/fjwqVqyI3bt3o1KlSqhUqRIcHBxw+/ZtBAQEwNTUFCEhIXj8+DEmTJigvOdPP/0EBwcHjB8/Xll2Xs/74v1eZGVllWtSyPPmzJmDvn37YsqUKZg8eTLi4+OVxzIzM/Hrr7/i5MmTKpNPbty4kWttzClTpgAAxo4dq7KUT05X/Ivn57SavSghIQHDhw/HhQsXEBAQkGfgA2RLXs74xrwUtXwhBMaPH4+2bduqLNnzvE6dOgEAvv32W5WtJvv27Yt58+Zh7ty5+O6771Rahf/55x+0bNkSrVq1UrvWZmHqkzNs4vr16yoBunfv3ujXrx+uXr2KNm3aqLznnN8nDx48UH4XHBwMIyMjfPvtt7C1tUXlypXh7OyMNm3awNTUFAAwbtw4Zf0yMjKQnZ2Npk2bAgBOnjyJL7/8Uhl209PT4e3tXezJYzqn9Ce0v750YTkBIsrtdd5WcsKECaJcuXLKP5scHR3FkCFDcp337Nkz0aVLF2FnZyfee+89sXTpUjFmzBhhb28vgoODxcGDB4WTk5OyHF9fX3H69Gnh4eGh/M7b21skJCSI7OxsMWTIEFGuXDnx0UcfiejoaOHm5qY8z93dXfzzzz8iICBAKBQKAUC4uLiIv//+Wwghl4Hp2rWrsLS0FHZ2dmLYsGEiJSVF5Xm3bt0qfH19haenp2jZsqUYOXKkePz4sfJ448aNlfdzcXER27dvF4GBgcLIyEgAEA4ODuLXX38t9nvdtm2b6NSpk6hUqZKoW7eu6Natm/jggw9UtqS8dOmS8PPzEwYGBgKA8PHxEQcOHFAeb9eunfKZExISREBAgDAxMVE+d+3atcWBAwfEnDlzhK+vrwAgFAqFqFixoqhfv76oVauWKF++vKhTp4747LPPiv33TlHLT0pKElWqVBEAhLGxsWjUqFGuMlesWKHy+87S0lLMmTNH5ZyzZ8+K/v37i3r16ommTZuKgIAA0bZtW/Hbb78Vqx45mjVrJszNzZX3rlq1qli8eLHy+NOnT0W1atUEAGFgYCDefvtt5fJGAISZmZn49NNPlefv379f+Pv7CxMTE1GpUiUxf/585bETJ04o34Ofn5948803xffff6883qFDBwFAODs7i6ZNm4rOnTuLGzduFFgHfVnCSCHEK9LxrweSk5OV/wJOSkpStgwQkXalpqYiKioK7u7uerG1IRG93grzZ5YuZA52lxMRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRkdZdvnwZw4cPh7e3t7YfhYg0hCGTiOg1NnHiRFSsWBEKhUL5MTU1hZOTEzp16oT9+/eX+DMkJCTgjz/+wMqVK5GcnFzi9ysNV69exbhx4+Dn54fGjRujRYsW6NWrF3bu3IkffvgBBw4cwOXLl1GrVi3le2/Xrl2B5X722WfK8318fHDw4EGsXLkSLVu2hEKhgK2tLZo1a4bAwEA0a9YMNWrUwNChQ3H+/Pli10WT5ffu3RtRUVGFOvf69esYNWoU3nrrLTRv3hwBAQFo2bIl1q9fDyFEsetDpUhQqUlKShIABACRlJSk7cchov979uyZuHDhgnj27Jm2H0Urnj59KszMzETZsmXFkSNHxKlTp0RISIiwtrYWCoVCrFy5UuP3vHbtWq5y/f39hZubm8bvpUl//fWX+Ouvv/I9Z+nSpcLc3FxMnz5dPHnyRPn9gwcPxODBgwUAsXnzZuX3I0eOVP7dcPr0abXlpqSkCAcHBwFA9OnTR+XYyZMn8/z+3Llzolq1asLCwkKEh4erLTs2NlYsWbJE7fGXLV8IIW7evCmMjIzEuHHj8j1PCCFWrlwpbGxsxM8//yyysrKU30dGRgpPT0/RqVOn1/rv0cL8maULmYMtmURErzkrKys4OjrCzMwMAQEBqFu3LsaNG4effvoJQgiMGzcO2dnZGr3np59+mqtMMzMzjd6jJFy7dg3Xrl1TezwsLAyDBw/GxIkT8fnnn8Pa2lp5rFy5cliyZAn69OmDx48fK7+vV68eKlasCAAIDg5WW/ayZctgbGwMAKhSpYrKMXt7+zyvqVmzJpYsWYKUlBRMmDBBbdlJSUk4dOiQ2uMvWz4ALFiwAJmZmVi2bBmSkpLUnrdlyxa8//77mD59OoYMGQIDg/+iio+PD3bs2IG9e/eiT58++d6PtI8hk4iIVP4iz9GlSxcAwKNHjxAbG6uxe4WEhGDdunUaK09XPHv2DEOGDEHZsmUxdepUtecFBwfjyZMnyp8bGBjgvffeg4ODAzZt2oTr16/nuiYrKwshISH48MMPldcUVoMGDQAAFy9eLPQ1RVGY8p8+fYrNmzdj2LBhSExMxPLly/M8LyUlBSNGjICtrS1GjRqV5zlVq1ZFUFAQtm7dit9+++3lK0AlhiGTiIjydOPGDQCAnZ0dypYti6ioKHTt2hUtWrSAm5sbWrVqpWzVi46Oxvfffw8fHx8MHDgQ+/btwwcffAAXFxe0atUKCQkJAIDQ0FCsXr0agAxbzZo1w7Zt23Ldd+zYsahWrRq8vb1x7tw5leP37t3DwIED0apVKzg5OaFr166IiYlROefIkSPo3Lkz3n77bbi6uqJPnz64ffs2ACA7OxtbtmxBx44dYWVlhfv37+OTTz5BQEAAXF1d8eeffxbrfW3cuBHx8fFo06ZNvq2yLi4u6NGjh8p3FhYWGDVqFLKysvDNN9/kumbTpk2oWrUq6tWrV+TnioyMBIBiXaup8n/55RcEBgZiwoQJMDAwwMKFC/McV7lz507cu3cPTZo0yfcdtm7dWlku6TCtdNK/pnRhfAQR5Zbf+CZfXyGcnXX74+v78u/Azc1NODk5KX9+8+ZN4e/vLwCIRYsWCSGEqFGjhujfv78QQoiYmBhhZmYmOnTooLzmwYMHAoDw9vYWhw4dEkIIcfXqVaFQKMTHH3+sPC88PFwAEMuXL1d5hqZNmwpra2uxdu1aIYQcg1i5cmXRokUL5TmPHj0Snp6eIiIiQgghxN27d4WTk5OoX7++yM7OFkIIsW/fPlGuXDlx/fp15bN6enoKFxcXce/ePWVZ7dq1EwDEwoULRWZmpsjOzhZNmjQRLi4uat/T8uXLcz13jpzxll999ZXa69WVOWPGDBEfHy8sLS2FqampiImJUTnH19dX7N27V+zZs0cAEDNmzFA5HhUVpXasZo0aNUT58uXF2bNn1T5DVFSU8tdW3fHilp+VlSWqVasmoqKihBBCtG/fXgAQ27dvz3XupEmTBADx0UcfqX0WIYQ4deqUACAcHR3zPe9VpS9jMo20kGuJiPTG/fvA3bvaforS8eTJE/Tv3x8xMTGIjY2Fh4cH5syZg8aNG+PJkye4cuUKhg0bBgCoWLEivLy8cPXqVeX1Dg4OAAA/Pz+89dZbAIBq1arBwcEBp06dKtQz2Nvbo3fv3gAAc3Nz1K9fX6Vlcf78+fDx8UHjxo0BAE5OTggKCsLcuXOxZ88etGrVCh988AF69eqlHLdYsWJFfP311+jatSs++eQTLF26VOV5n++W9ff3R0REBB4+fAiFQoHu3burPN/9+/cBACtWrFD5fv369cqW0rJlyxaqrnnVffDgwZg/fz7mzZunbNHct28fAKBFixbYu3dvvmVs3rwZly9fhpGREW7cuIGHDx/CwsICc+bMwRtvvKE8r0+fPrj73G/s1NRUREdHo1mzZirlTZkyBW3bti1y+c8LCwtDzZo1UblyZQDAyJEjsXPnTsyfPx8dOnRQOTdnrKqFhUW+9cwZ65rTQk66iSGTiCgfFSpo+wkKpqlntLW1xcqVK/M8ZmNjg0OHDsHHxweZmZnYtWsXYmNjYWhoqDxH3ThBCwsLPHv2rFjPZG5ujqdPnyp/vmfPHty5c0clDCUkJMDNzQ13797F8ePHce3aNVSvXl2lnC5dusDKygpbt25Vhsy8njcn3Dx79gxubm44cOCAyvGccDlgwIBc1+Z076akpBS1mkofffQRFi9ejB9//BHTpk2DnZ0dvvnmG0yePLlQ13fr1g1r1qwBIMdxHjlyBOPHj8fIkSOxdu1ahIeHw8TEBGvXrlW5Ljo6GjNnzswVnotb/vNCQkIwa9Ys5c/btm2LKlWqYM+ePTh//rzK2qhlypQBAJUxq3nJ+f2U8w8F0k0MmURE+fjnH20/ge6oW7cuQkJC8Pfff2PgwIFwc3NTtt4VRBRzXUOFQqEyC/3hw4do3bo1lixZkuf5GzZsAIA819usXLkyLl26VKj7Fud569Spg61btyrHshaHq6srgoKCsGrVKixevBgdOnTAjRs30K1btyKXZWhoiEaNGmHPnj2oVq0a/v77byxduhQjRowo9vMVtfwTJ07gzJkzmDlzpsq1Oe93wYIF+Omnn5Tf+/n5ASh4ktLNmzcBAAEBAZqoCpUQTvwhIqICJSYmIiAgAFevXsW2bdvw7rvvqrRilhZbW1v8+eefuZbAEULg/PnzqFSpEgCodOPnsLGxQbVq1Urs2Xr37g0DAwP8/vvvyMrKyvfce/fuqT02adIkKBQKzJ8/H59//rlyskxx2djYoEmTJgCACxcuFLuc4pQ/d+5cLFy4EAcOHFD5HDt2DJaWlli9ejUePXqkPL99+/ZwdHTEoUOHkJiYqPaeOUMIhgwZovH6kOYwZBIREbKzs5GZman2+KpVq3DmzJl8A09hW/8UCkWxnhEAmjdvjlu3biEwMBB37twBAKSlpWHSpEl4+vQpfH194erqit9++w2pqakqz3b9+nXleM+iPG9hVa9eHWPHjkV0dDTmzp2r9ryc1r0c2dnZKq213t7e6NChAx4+fIjDhw+jf//+Kuc+/98c+dVFCKFsGaxbt27RKvUS5V+/fh0nT55E3759c13j6OiIoUOH4tmzZ1i8eLHye0tLS8ydOxfPnj1Tu2bo7du3sWzZMvTs2bNQuySR9jBkEhG95hITExEbG4v4+Hi162FaWloCkAEJkN2ZUVFRSElJQVZWFqKiopRd5zmTYwAgPT0dSUlJuH//vjKo5EyMiYmJQXZ2No4cOYLs7GzExMQgMTER6enpyutzWrlyWv7Gjx+PihUrYvfu3ahUqRIqVaoEBwcH3L59GwEBATA1NUVISAgeP36MCRMmKO/5008/wcHBAePHj1eWndfzvni/F1lZWcHKykrtu5wzZw769u2LKVOmYPLkyYiPj1cey8zMxK+//oqTJ0+qTKa5ceNGrrUxp0yZAgAYO3asylI+OV3xL54fFxeX5/MkJCRg+PDhuHDhAgICAvIMfABgZGSU7/jGopYvhMD48ePRtm1bGBnlPTKvU6dOAIBvv/1WZavJvn37Yt68eZg7dy6+++47lVbhf/75By1btkSrVq3UrrVJOkQbU9pfV7qwnAAR5fY6bys5YcIEUa5cOeWfTY6OjmLIkCG5znv27Jno0qWLsLOzE++9955YunSpGDNmjLC3txfBwcHi4MGDwsnJSVmOr6+vOH36tPDw8FB+5+3tLRISEkR2drYYMmSIKFeunPjoo49EdHS0cHNzU57n7u4u/vnnHxEQECAUCoUAIFxcXMTff/8thBDixo0bomvXrsLS0lLY2dmJYcOGiZSUFJXn3bp1q/D19RWenp6iZcuWYuTIkeLx48fK440bN1bez8XFRWzfvl0EBgYKIyMjAUA4ODiIX3/9tdjvddu2baJTp06iUqVKom7duqJbt27igw8+UNmS8tKlS8LPz08YGBgIAMLHx0ccOHBAebxdu3bKZ05ISBABAQHCxMRE+dy1a9cWBw4cEHPmzBG+vr4CgFAoFKJixYqifv36olatWqJ8+fKiTp064rPPPiv23ztFLT8pKUlUqVJFABDGxsaiUaNGucpcsWKFyu87S0tLMWfOHJVzzp49K/r37y/q1asnmjZtKgICAkTbtm3Fb7/9Vqx6vEr0ZQkjhRD6s8t8eno6QkJCsHz5cmRmZsLFxQVffPGFcixIYa53dnZW+y+yHA8fPoSjo2Ou78+ePYvZs2fDwsKiWAvAJicnK/8FnJSUpGwZICLtSk1NRVRUFNzd3fVia0Mier0V5s8sXcgcetNdnpaWhrZt22L16tXYs2cPrl+/jlGjRqFly5YIDQ0tVBlhYWEFBkx/f/9cAfP48ePo3r076tSpg/Xr1xc4oJuIiIjodac3IXPy5MkIDw/H8uXL4erqCgDo3r07AgMDMWDAAJXxHOosXboUY8eOxZkzZ3D//n3ExsYqPzExMbC2ts618C4gByhv2LAB/fr103i9iIiIiF5FehEyo6OjsWjRInh5eaFBgwYqx/r27YuUlBRMnTo13zKioqLQvHlzfPfdd6hduzbKly8PBwcH5ScyMhJPnz7NM2S6u7vDwMAA/v7+Gq0XERER0atKL0Lmhg0bkJmZiYYNG+Y6lhP8wsLCVGbxvcjJySnfHRNCQ0Ph7++vbCXNC8dqERERERWOXoTMHTt2AIByH9rn2dvbw9nZGenp6Th8+LDaMkxNTdWu7ZaRkYGtW7eiR48e+T7Hy6ztRkRERPQ60YuQefr0aQCAi4tLnsft7OwAAJGRkcUqf9++fXj8+DECAwOLdT0RERERqdL5vctTU1OV24flhMkX2draAlC/WGxBCtNVXlxpaWlIS0sDkPdeukRERESvIp1vyXx+nKWFhUWe5+R0gz+/hVhhZWZmYsuWLXlO+NGE2bNnw9bWFra2tnByciqRexCRZujRssFE9BrTlz+rdD5kmpiYKH+s7qXmbEFmb29f5PJzuspLKmROnToViYmJSExMRExMTIncg4hejqGhIQA5PpuISNdlZmYCgNotO3WFbj8dZHA0MTFBenq62u7mhIQEAMh331V1crrKK1Wq9DKPqZapqSlMTU0B/PcXGRHpFmNjY5iamiIxMRHW1tac5EdEOu3JkycwNDTU+Vyh8yHT0NAQXl5eiIyMVNsS+ODBAwBAnTp1ilR2Tlf5xx9//LKPSUR6zsHBAXfv3sWdO3dga2sLY2Njhk0i0ilCCCQnJ+PJkyeoWLGizv8ZpfMhEwDatGmDyMhInD9/PtexuLg4JCYmwtLSEo0bNy5Sufv378ejR484q5yIYGNjA0D+mXL37l0tPw0RUd4UCgXs7OyUk551mV6EzEGDBmHOnDmIiIjIdezIkSMAgG7duim7pQsrNDQUAQEBJdZVTkT6xcbGBjY2NsjIyEBWVpa2H4eIKBdjY2Od7ybPoRch08PDA0OHDsWPP/6IyMhIlW7xlStXwtzcHDNmzFB+Fx4ejilTpqBPnz4YM2ZMnmXmdJVPmzat0M+RM9CWf/kQvdqMjY1hbGys7ccgItJrOj+7PMe3334LX19fDBs2DI8ePYIQAgsXLsTvv/+OVatWqewGNHfuXBw/fjzfsZbh4eGIj48vUlf5sWPHAABnzpwp1nJJRERERK8LvQmZlpaWCA8PR0BAAPz8/ODh4YF9+/bhxIkTuYJiUFAQrK2t0b9/f7XlFaWrfP78+ShbtiyWLl0KADh37hwcHBzyLZ+IiIjodaYQ+rKi5ysgOTkZVlZWAICkpCRYWlpq+YmIiIjoVaQLmUNvWjKJiIiISH8wZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxulVyExPT0dwcDA8PT1RtWpVNG3aFBEREUW63tHREQqFIt9PbGxsrms3bdqE+vXro0qVKqhduzaWLl2qyaoRERERvVKMtP0AhZWWloZ27drhwYMH2LNnD1xdXREaGoqWLVti7dq16N69e4FlhIWFIS4uLt9z/P394ejoqPLdtGnTsHDhQmzfvh1NmzbFpUuX0KRJE5w9exYLFix4qXoRERERvYr0piVz8uTJCA8Px/Lly+Hq6goA6N69OwIDAzFgwABERUUVWMbSpUsxduxYnDlzBvfv30dsbKzyExMTA2tr61xhdcuWLZg9ezamT5+Opk2bAgBq1KiBL7/8EgsXLsTGjRs1X1kiIiIiPacQQghtP0RBoqOj4eHhgerVq+P8+fMqx3bt2oX27dujZ8+eWL9+vdoyoqKisH79ekydOjXP4znl3Lx5Uxlis7OzUaNGDVy7dg33799HuXLllOcnJSXBzs4OFSpUwM2bN2FoaFhgPZKTk2FlZaW83tLSssBriIiIiIpKFzKHXrRkbtiwAZmZmWjYsGGuY/7+/gBkV3h8fLzaMpycnDB58mS1x0NDQ+Hv768MmABw4sQJXL16FVWrVlUJmABgZWUFb29v3L17Fzt37ixqlYiIiIheaXoRMnfs2AEAqFKlSq5j9vb2cHZ2Rnp6Og4fPqy2DFNTUxgY5F3djIwMbN26FT169Cj0fQGgVq1aAIDw8PCCK0FERET0GtGLkHn69GkAgIuLS57H7ezsAACRkZHFKn/fvn14/PgxAgMDS/W+RERERK8qnZ9dnpqaiqSkJAD/hboX2draAkCBM8fVyaurHIByKaOXuW9aWhrS0tIAyPERRERERK8DnW/JfH6cpYWFRZ7n5HSDp6amFrn8zMxMbNmyJc8lkHLu/TL3nT17NmxtbWFrawsnJ6ciPx8RERGRPtL5kGliYqL8sbqJ8Onp6QDk+Myiyukqzytk5tz7Ze47depUJCYmIjExETExMUV+PiIiIiJ9pPMh097eXhn21HU3JyQkAAAcHByKXH5OV3mlSpVyHatQocJL39fU1BQ2NjbKDxEREdHrQOdDpqGhIby8vABAbUvggwcPAAB16tQpUtk5XeUvzirPUbt27RK5LxEREdGrTudDJgC0adMGAHItxA7ISTeJiYmwtLRE48aNi1Tu/v378ejRo1yzygtzXwC4du0aAKBdu3ZFui8RERHRq04vQuagQYNgYGCAiIiIXMeOHDkCAOjWrRtMTU2LVG5oaCgCAgLy7CoHgJYtW8Ld3R0XL15UzjTPkZCQgIsXL8Ld3R0BAQFFui8RERHRq04vQqaHhweGDh2Kc+fO5VqTcuXKlTA3N8eMGTOU34WHh8Pf3x8LFixQW2Z+s8pzGBkZYfbs2cjOzsbatWtVjq1ZswbZ2dmYNWtWobaUJCIiInqd6MXe5YCcfNO0aVMYGRlh586dKFOmDL7//ntMmDABa9euVeny7tixI3bs2AErKys8ffo0z/L27NmDNm3a4ObNm2pbMnMMGzYMmzZtwv79+1G7dm389ddf6NixIwYNGoSQkJAi1UHb+4gSERHRq08XMofOL8aew9LSEuHh4Zg+fTr8/PxgYGCAmjVr4sSJE8oJOjmCgoIQERGBfv36qS2voK7y5/3www+oWbMmevXqhbS0NJQvXx6rVq1Cly5dXrpeRDrj2jXgyhWgQQOgGCs1EBERPU9vWjJfBbrwrwqiXO7fBz7/HPjtN6BuXeCffwBbWyAgAOjQAejUCfj/71siItIPupA59GJMJhGVkLQ0wNMTMDYGLl8Gdu0CHj4Edu8GWrcGNm4EXF2BQYOAU6e0/bRERKRHGDKJXmempkC/fkBSkmy9BACFAqhaVX4fFgZcvw7UrAn06AE0bgxs2gRkZmr3uYmISOcxZBK97r7+Gjh8GNi6Ne/jZcoA48bJls5Jk4AffwSqVQPmzAEePSrdZyUiIr3BkEn0urOwAFavBoYPBy5eVH+eoaEcn7l3L/D773KiUPXqwIgRwO3bpfe8RESkFxgyiQioXx/47jugVSvgwoWCz69VC/jpJzkbvVw5wNcX+OAD4MaNEn9UIiLSDwyZRCT16AEsWCAn/BQmaAKAvT0wc6YMm5UqAQ0byrGcly6V6KMSEZHuY8gkov+8+65s0WzTBjhzpvDX2dkBn3wiu9B9fIAWLWRovXy5pJ6UiIh0HEMmEakKDAQWLZJBc/v2ol1rZQWMHy9npDdqBDRrJsds3rtXIo9KRES6iyGTiHLr3Bn44w9g2DBg5cr8z01LA5Ytk7PTb90ChADMzIAxY2S3eZkyQO3awKhR8jgREb0WGDKJKG916gAHDwJffCGXOcprc7BTp2T3+NatwIoVQJMmgKOjDKnbtgHW1sCsWTJsli0L+PkBgwcDd+6Udm2IiKiUMWQSkXpVqwKHDgFbtgBBQUBKiurxmzflQu7Ll8uF26Oj5TjMPn2A2bNlUN2wQY7Z/Owz4OpVOUGoXj3gyy+BZ89Kv05ERFQqGDKJKH8VKsgWTRsbOcby+fGV77wD9O8vJ/nk7AJUtizQsyfw99/AvHlyqaOqVWWL5rNnwIwZsgX04kXA21uG0OxsrVSNiIhKDkMmERXMxESGxZ49gTffVJ15/sUXcqH2xYtVr1Eo5Czz/fuBHTuA+/fl+po9e8ofr10LrFkDLFwoWza3b8+7S56IiPQSQyYRFY5CIWeOz58vZ54vWya/NzCQAfPrr+XOQXnx9pZhMjoaaN4c6N5ddr9XrAj89RcQHAxMny7L/fffUqsSERGVHIZMIiqaLl3kXufffQeMHAmkp8vu8AMH5LjL4GD1LZKWlnJnoIsXgbp1gYAA4MMPgRo1gH/+kcsntWkjJwfFxJRipYiISNMYMomo6KpWlWMuHz6UXeIPHgAeHjJ8bt4MfPRR/l3fZmbApEnA+fOAra0Mm336yOB56RLg4iInDX36KfD0aalVi4iINIchk4iKx8oK2LgR6NBBLk0UHg6ULy//e+wYMGBA7tnoL3JwAD7/XO553rSp7EIfMEB+zpyRYzffeAP49VeO1yQi0jMMmURUfAoFMGWKnMTTr59clsjCQk72MTWVk4SuXi24HAsLYPhwuWf6m28C/v5yotF338k1OL/7TraYFnZPdSIi0jqGTCJ6eU2aACdPyqWO2reXXdw//yzHWzZuLHcPykt0tJxAtH8/EBUlJxFNmABERsqfv/GGXO4oIgLo1Qto2RIYOpTbVBIR6QGGTCLSjHLlZJh8803A1xfYuxd4/325NNGgQUBISO71MNu1A+bMkZOFWreW4zPff18eW7lSLvC+datc+sjNTY7XrFBB7jL0zTdy0hEREekkhkwi0hxDQ7nY+po1ssVxzBjAyws4ehT4/XegbVvVWeOrVgEJCcAnn8hu9YcPAVdXGSK/+kpeu3078MMPslX0vffkwu///CPLrF0b2LVLW7UlIqJ8MGQSkeY1aSK7vJOSZKtmXJxs2WzZUk4SOnhQnle/vpw81LOnnOhjaSmXQfrnH7leZvXqcpznG2/I423byiWOPvkEmDsX+P572b3esWPhxn4SEVGpYcgkopJhYyPHW371lRynuWiRDIQbNshZ5PPmye7zxo3l+M2OHeXYS0C2Zq5bJ1sp4+Lkckb9+smZ7FeuyNAZEADs2SOXUmrdWpYzZUrBM9qJiKhUMGQSUcl65x3gyBE5tvLttwEnJ9nVvXOn3Av9+nWgUydgxQqgb185RjNnuSJvbzmz/OZNufVkgwZyyaOcmehxcfJ7T0/Z8nn/vuxC37tXixUmIiKAIZOISkPlyrLVsVcv4K235Gzy3btli2ajRjKAtmgh19fcsEGO5czK+u96c3O5ePu5c0BysmzJXLNGbmf5yy/y/FGjgJkz5dJHw4fLsm/d0laNiYheewyZRFQ6DAxk+Dt4ULZO9usnx2Lu2iX3RJ8wQc4uP3BAtm6++y4QG6taRrlychzmwYNyz/Pq1WWQjIyUXer+/jLMHjsmZ6T7+QFffMFZ6EREWsCQSUSly9NTdpc7Ocmu7ago4MQJGSjr1JFbTW7bJkNinTpyB6EXVa8ObNokW0AXL5aBNChIdpk/eCDDpq+vDJ///iu71HPGexIRUalQCMG92kpLcnIyrKysAABJSUmwtLTU8hMRadmJE7JF8623ZOvmwYP/LX00caLcCz0oSC5fNH683GHoRVlZwIIFcoJRUBAweTJw8aK8plIl4NtvZZD98EM5m33OHLk3OhHRK0wXMgdbMolIe+rXlzsFmZjI1sayZWXw/PNPuVxRlSrA8eNyUfaePYHHj3OXYWgIjBsnF2q3sUFqnTpYHRyMbm5uaHbjBrr5+WF1SAhS//wTqFlTtnAGBwMZGaVfXyKi1whDJhFpl4WF7PKeO1cutD53ruwGb9lSBsI//pAThSpXll3oYWF5l1O2LLY1aACnjAz027cPW3buxMErV7AlPR39wsPhVK0afn/2TIbYo0flTPXIyNKsKRHRa4Xd5aVIF5quiXRaQoJslTxyRG4raWsLDBgAODjItTTv3gUGDpSzyxcuBMqXV166bds2dO3aFQCQ1x9rCoUCEAJbmjZF502bgH375L0GDwY+/hgwNS2dOhIRlQJdyBxsySQi3WFnByxfLsdNdusmg+bevXKZo3r15K4+//wj18+sUwfYsQMAkJqaigEDBgDIO2Aqv1coMODYMaTWrAkkJgKnT8sya9WSSyoREZHGsCWzFOnCvyqI9MajR8Do0TJULlokWy3795fLGC1YIENinz5AzZpY3bAh+k2cWOiiVwcH471du+SySitWANeuyeWV3npL7kRka1ty9SIiKgW6kDnYkklEusneHli7Vq6LOWIEMHs28PvvQOfOcm/0sDA5vrJxY2z55BMY5DXzPA8GAMI2bpQtpJ07y7GZly7JCUh2drJVc/Pm/3YdIiKiYmHIJCLd1qoVcPasXBvTz0/OQD97FrhzB3jzTaBpU8T7+iK7kKEwG8Cj69fl3ue+vnL9zK1b5d7ngYFAaCgwaxbQrp3sSiciomIx0vYDEBEVyMxMbhn5zjtyItCaNcD8+XJ2eMeOKGthAQMDA2RnZxdYlIGBAexbtJD7pPfvL2e0b98uP717y6AZESG70Rs3lutujhkjl0oiIqJCY0smEekPHx/ZRd6qFdCwoWxpPHsWXatVK1TABIDs7Gy88847QNeusov84kXZZV6+vAytDx8CdesCXl5ye8o//pBjNf/9t0SrRkT0quHEn1KkC4NwiV4Zd+8CH3wAPHyI1B9+gNPbbyPh6VPk9weaQqGAnZ0dYmJiYGZmJr8UQnaXjxsnx3p+/TVw7hwwbBjQtKmc6b5jh2zR/OADYOpULndERDpPFzIHWzKJSD85O8uJQKNHw6xjR6xs2xZQKKBu+k/O9ytXrvwvYAJyq8quXeWe6ZUry6WRkpNl0HRwkK2njo6ylfPSJbmU0unTJVkzIqJXAkMmEekvhUKOrTxzBp2srLDF0RF2///Xes4fbgYG8kd2xsbY6uiITvfvA+npucuysAA++0yulzlxIjBjhtwPffNmuZTS7Nly3c7PPwc6dJA/z8oqpYoSEekfhkwi0n/lygHLlqHz5s2IcXPD6rp10bVtWzTz9kZXMzOsDghAzJUr6LRzp5zgU6sWcPBg3mXljPu8dg1o3hxwdQVOnQLi4gB/f6BaNTmW86+/ZPf6hQulW1ciIj3BkElEr45GjWAWGYn3+vTB5lOnEN63LzbfvYv32rWD2ZtvAocOARs3ysXcBwwA+vUDLl/OXY6dHfDbb3Idzbp15WzzNWvkuMx27YAffwS2bQMGDQJatJBLHrFVk4hIBUMmEb1ajI2B8eOB48fl2ElfX7kN5YEDMiy+8YbcLejsWdmi+fbbcv/yhw9Vy1EogAkTgF275GLwCxcCQUHyuhMngLZtZbd5ZKQMr82bA7duaaPGREQ6iSGTiF5Nbm7A+vVyHOVXXwEffgh8+63cReibb4Du3eX+6FeuyOWLatWSW0pmZKiWU7cu8PffwC+/AGPHylbO7dtlqPT1BQ4flrPP330XqF8fWLwYKORySkRErzKGTCJ6tTVqJNe7bN1a7vJz8KD8tG8vdwyaNw+YNk0GyYMH5ZjMZcuAJ0/+K8PFRY7BvHlTrs/58KG8JixMThAKCgLef1+WsWmTXPooOlprVSYi0gUMmUT06jMykutgnjghu7dr15Ytnf/8Iyf4VK8O7Nkjt5RctEj+2N1dhsfDh2UZ1tYyVHbrJheDj4+XLZcnT8rllN58U7Zg7tsH9Oolf75mDfdAJ6LXFhdjL0W6sDAqEUG2So4eDVSoILenTE+XLZOXL8tJPIGBsiUzNFQuzl6rlvyvh4e8fuZM2UW+bx9gYyO/W7UKmDQJ+PRTYPhwue7mgAEygP74I1CxorZqS0SvIV3IHGzJJKLXT+PGshWzc2fZtb12rfz88osct9m8OXD9upwQdP68XKqocWO5h/mmTXKyUPnysiXz6VNZZr9+srv811+BNm3kAu5Hjshlj+rWBdat026diYhKGUMmEb2ejIzkrPF//5WzzT09ZfA8cEAGxm7dgE6dZMj88EO5HmbZsnJpoy++kOMzk5NVJwpVqSKvb9xYfq5ckS2ke/cCwcHAwIFASoqWKkxEVLr0KmSmp6cjODgYnp6eqFq1Kpo2bYqIiIiXKvPx48cICQlB165dMXToUMycORMZL8wuvXXrFgYOHIgqVaqgYsWK8Pb2xoIFC5DFdfGI9J+DA/DDD3LSz9GjgJcXYGsru867dgW6dAGGDAEyM+Ukn3XrZGg8dw64cwewt1ctz9AQmD5d7gzUooVsHfX2lpOPjI1l6+f581qpKhFRqRJ6IjU1Vbz99tvCy8tL3Lx5UwghxMaNG4WxsbHYuHFjscpcu3atcHR0FB9//LF4+vRpnuecPHlS2NnZiQkTJoi0tDQhhBDr168XxsbGonv37iIrK6vQ90tKShIABACRlJRUrGcmohL2999C1K0rROvWQly8KMTTp0JMmyZEuXJChIQIkZJS+LKuXhWiXj0hgoJkOUII8euvQpQvL8QPPwiRnV0ydSCi154uZA69CZljx44VAMSxY8dUvg8KChIWFhbixo0bRSpv6tSpwszMTPzxxx9qz0lOThZubm6iQYMGuY59+eWXAoCYM2dOoe+pC7/gRFQImZlCLFokRIUKQgwZIsTdu0JcuyZE9+4yIH78sfyuMFJThRg0SIhmzYR4/Fh+d+2aEAEBQvTo8V/4JCLSIF3IHHrRXR4dHY1FixbBy8sLDRo0UDnWt29fpKSkYOrUqYUuLzg4GLNnz8bq1avRpk0bteetWbMGN2/eRPv27XMdGzlyJIyNjTFr1iwkJSUVvjJEpPsMDeV4zcuX5Qz0OnWA5cuBpUtll3pqqlxPs29f4MaN/MsyNQV+/lmu0dmwIRAVBVStKrvny5WTE4Py2tqSiEjP6UXI3LBhAzIzM9GwYcNcx/z9/QEAYWFhiI+PL7Cs3bt3Y9q0aejZsycCAwPzPXffvn0AACcnp1zH7Ozs4Ovri4SEBPzxxx+FqQYR6RsbGzm28swZIC5OTg7atUtO4omKkksbvfmm3AkoNlZ9OQYGwOzZcrvLRo3krHMTE7lV5dSpcob78uVcU5OIXil6ETJ37NgBAKhSpUquY/b29nB2dkZ6ejoO5yyarEZGRgbGjh0LIQRmzJhR4H0fPXoEAEhMTMzzuJubGwDgxIkTBZZFRHqsYkW51uX+/XJLyTfekLPMP/oIuHhRBkYvL7nk0d9/qw+LgwbJ9TS7dv0vlL73npyR/v338nh6emnVioioROlFyDx9+jQAwMXFJc/jdnZ2AIDIyMh8y9m4cSMuX76MBg0a4OrVqwgKCkK9evXg5uaGPn364MYL3V7Ozs4A1IdI8f+/SOLi4gpbFSLSZ2+8IRdhX7VKzjL38gIiIuRe6JcuyZbNYcPk93PmAAkJucto0UKeM2AAkLNCRY0awKFDwLNnQMuW+beKEhHpCZ0Pmampqcoxjzlh8kW2trYACg57oaGhAIDY2FgkJSVh2bJlOHr0KEaNGoV169ahQYMGuHDhgvL8d999FwCwbds23Lp1K1d5d+/eBQCYmJiovWdaWhqePHmi/BDRK+DNN4E//gCWLJFrZrZoAdy6JbvNz5wBVq+Wa2R6eMju8AcPVK//9FO5BeUnn/z3nbm5DK5t2shljl5yeTYiIm3T+ZD5/DhLCwuLPM8xMJDVSE1NzbesgwcPAgBCQkLQu3dvmJubw8TEBBMnTkSfPn0QHx+PAQMGKM/v3Lkz+vbti9TUVHTs2BHn/7+2XXR0NCZPnqxs4czpNs/L7NmzYWtrC1tb2zzHdhKRHmvaVO6H3r+/3D2oZ0/g9GnAz08G0DNn5GLtNWsC7dvL7Sh/+w24ehV46y05tvP//1gFACgUwMcfA8uWAX36AIsXa61qREQvS+dD5vOthELNOKf0/49hsn9xUeTnJCcnI+H/XVc53eDPGzFiBADZNX7+uYWSV65cicWLF8PMzAxt2rRB8+bN8cMPP+CNN95Q3vfNN99Ue9+pU6ciMTERiYmJiImJUXseEekpAwMZMq9ckaGze3egVStgzx45lvPbb+UWlSNHylnrv/4qx2T++68MpHn8eYTmzeXYzkWLZKsnJwQRkR4y0vYDFMTe3h4mJiZIT09HcnJynufkhEcHBwe15TzfVW1jY5PreMOGDWFnZ4eEhARcvHgR3t7eAACFQoHhw4dj+PDhKuePHTsWgJx53rhxY7X3NTU1hampKQDA0NBQ7XlEpOfMzeWyR0OHyv3NJ0yQOwcFB8ulizp0kJ/CqlQJ+Osv2UJ65YpcPsnKquSen4hIw3S+JdPQ0BBeXl4AoLYl8MH/xzvVqVNHbTkODg5QKBQAoHZsZM7Eouzs7HyfKS4uDqtWrQIAjB8/XtldT0QEIyOgVy/ZSjl8uNwHvWNHGRiL2iJpby9ntDs4yHGgL47tJCLSYXqRjnIWTD+fx36/cXFxSExMhKWlZb4tisbGxqhdu7bacgDAzMwMAFC9evV8n+eTTz5BQkICatWqhdGjRxeqDkT0mjEwAIKCgAsXgG7d5Ixyf39gwwa5D3phmZjI5Y369ZMzz7maBRHpCb0ImYMGDYKBgQEi8phteeTIEQBAt27dlN3S6vTq1QsAsHPnzjyPR0dHo2rVqvDx8VFbxo4dO/Dzzz/D3t4emzdvhrGxcWGrQUSvIxMT4P335RjMzz8HfvkFqFZNLsT+7Fnhy5k4EejRQwbNe/dK7nmJiDREL0Kmh4cHhg4dinPnzuVaC3PlypUwNzdXWVw9PDwc/v7+WLBggcq5o0ePhouLC8LCwnDt2jWVY9u3b0dcXBxmzZql7FZ/0d69e9GzZ0+UK1cO+/btg4eHh2YqSESvPoUCaNsW+PNPYOtW4PBhGTaDgwu/LuYnn8gWzTffLHg7SyIibdPKjunFkJSUJHx9fYW/v7+Ij48X2dnZYsGCBcLExESEhoaqnNuhQwcBQFhZWeUq5/Tp08La2lrUrl1b3Lx5UwghxL///isqV64sJkyYkOe9T58+LYYNGyYMDQ1F27ZtRUxMTLHrAC1vVk9EOuTCBSGGDxeibFkhevQQ4s8/hcjKKvi6n34SwsdHiOTkEn9EItJPupA59KIlEwAsLS0RHh6OgIAA+Pn5wcPDA/v27cOJEydy7UEeFBQEa2tr9O/fP1c5derUwdGjR+Hu7g4fHx94enpi6NChCA4Oxpw5c1TOXblyJWxsbNCpUyc8fvwYO3fuxK5du1CxYsUSrSsRvSbeeEOuhRkVBbRuDcyYAVSpAnz5JfDwofrrhg6Va3EOH87ljYhIZymE4J9QpSU5ORlW/1+CJCkpCZaWllp+IiLSOefPAz/9JNfTbNdOjuds1Ah4cfx3aqrsNh8xAhgyRDvPSkQ6Sxcyh86vk0lE9Frx9gYWLJCtmatWyXGY587JfdH9/YHq1eW6mWfPApGRqjsGERHpELZkliJd+FcFEemhZ8+AU6eAY8eAa9eAGjWA2rVl8CxbVttPR0Q6SBcyB1syiYh0nbm53Ov8rbe0/SRERIWmNxN/iIiIiEh/MGQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxjFkEhEREZHGMWQSERERkcYxZBIRERGRxpVayDx06FBp3YqIiIiItKxUQmZaWho6depUGrciIiIiIh1g9LIFxMTEYOPGjYiOjkZycjKys7MhhFAez8jIwMmTJ/HkyZOXvRURERER6YmXCpm///47evTogfT0dJVgmReFQvEytyIiIiIiPfJSIXP8+PFIS0tDnTp10KtXL5QtWxZGRrmLPHXqFL7//vuXuRURERER6RGFKKgJMh82NjZQKBSIiYmBpaVlvuc6OTkhJiamuLd6JSQnJ8PKygoAkJSUVOA7IyIiIioOXcgcLzXxp02bNrC1tS3Ug0dERLzMrYiIiIhIj7xUyPzuu+9gaGiI48ePF3juiBEjXuZWRERERKRHXipkOjs749ChQwgJCcGjR4/yPEcIgcOHDyM8PPxlbkVEREREeqRQE3+sra2RkpKS7zmhoaEaeSAiIiIi0n+Fasns06cPhBAv9SEiIiKi10ehQubo0aPh6+uLmJgYZGRkIDs7u9Cf9PR07Nq1i+tkEhEREb1GCtVd7u3tjX79+qFChQpFv4GREdq0aYO+ffsW+VoiIiIi0k8vtU4mFY0urFlFRERErz5dyBwvNbuciIiIiCgvDJlEREREpHEMmURERESkcQyZRERERKRxDJlEREREpHEMmURERESkcQyZRERERKRxDJlEREREpHEMmURERESkcQyZRERERKRxDJlEREREpHEMmURERESkcQyZRERERKRxDJlEREREpHEMmURERESkcQyZRERERKRxDJlEREREpHF6FTLT09MRHBwMT09PVK1aFU2bNkVERMRLlfn48WOEhISga9euGDp0KGbOnImMjAyVc27evIn3338fLi4ucHV1RaVKlTBw4EDcvn37pe5NRERE9KrSm5CZlpaGtm3bYvXq1dizZw+uX7+OUaNGoWXLlggNDS1WmevWrYOnpycePXqENWvW4Oeff8bMmTNhbGysPOfq1avw9fXFo0ePEBkZiVu3buHkyZO4efMm/Pz8cOPGDU1VkYiIiOiVoRBCCG0/RGF8+OGHmD9/Po4dO4YGDRoov+/duze2bt2Kf//9F+7u7oUub9q0aZg3bx62bNmCNm3aqD2vU6dOOHz4MG7fvg1LS0vl97dv34arqyt69OiBDRs2FOqeycnJsLKyAgAkJSWplEdERESkKbqQOfSiJTM6OhqLFi2Cl5eXSsAEgL59+yIlJQVTp04tdHnBwcGYPXs2Vq9enW/ABID9+/fDw8Mj1y9OpUqV4ODggHPnzhW+IkRERESvCb0ImRs2bEBmZiYaNmyY65i/vz8AICwsDPHx8QWWtXv3bkybNg09e/ZEYGBggedbWlriwoULePLkicr32dnZSElJQZ06dQpXCSIiIqLXiF6EzB07dgAAqlSpkuuYvb09nJ2dkZ6ejsOHD+dbTkZGBsaOHQshBGbMmFGoe3fu3BlJSUkYOXKkyve7d++GoaFhocshIiIiep3oRcg8ffo0AMDFxSXP43Z2dgCAyMjIfMvZuHEjLl++jAYNGuDq1asICgpCvXr14Obmhj59+uQ5iefLL79EpUqVsGbNGowZMwbZ2dl4+PAhZs+ejX379sHT0/Ol6kZERET0KjLS9gMUJDU1FUlJSQD+C5MvsrW1BQDExcXlW1bOLPTY2FgkJSVh2bJlMDQ0xPz58zFp0iTs3r0bERER8PLyUl5ToUIFhIeHo2XLlli4cCHu3LkDW1tbbN26FWXKlCnw+dPS0pCWlgZADsIlIiotaWnAtGlAZCRw9ixgaAh4egJeXkDbtkDr1oC5ubafkoheVTrfkvn8OEsLC4s8zzEwkNVITU3Nt6yDBw8CAEJCQtC7d2+Ym5vDxMQEEydORJ8+fRAfH48BAwbkuq5q1apYu3YtxowZg/DwcKxYsQKzZs1CdnZ2gc8/e/Zs2NrawtbWFk5OTgWeT0SkKRkZQEgIMHo0cOkScO4cMGsWUKsW8MMPgLMz0LMnsGED8PSptp+WiF41Oh8yTUxMlD9Wt9pSeno6ADk+U53k5GQkJCQAAJydnXMdHzFiBADgxIkTOH/+vMqxvXv3YteuXZg/fz4OHToEJycnzJ07F7179y4waE6dOhWJiYlITExETExMvucSEWmSlZUMkbdvA2XLAo6OQKNGwIgRwB9/ANevAx07AuvXA25uQI8ewJ9/AoX49zMRUYF0PmTa29srg6a67uac8Ojg4KC2nOdnh9vY2OQ63rBhQ2V3/MWLF5XfHz58GIGBgRg/fjwAwNvbGwcPHkT58uWxYcMGfP311/k+v6mpKWxsbJQfIqLS9PnnwFdfAWfO5D5WpgzQty8QFgbcugW0awfMnAlUrQrMmCFDKBFRcel8yDQ0NFSOkVTXEvjgwQMAyHc5IQcHBygUCgDItRxRjpyJRTmtk9nZ2Rg6dCh8fX1VxoNWq1YNW7ZsgZGREYKDgwvspici0pbq1WXXeNeuQFSU+vOsrID33wf+/hvYsQNITQWaNAFatQJ27mTrJhEVnc6HTADKBdNf7MYG5GSfxMREWFpaonHjxmrLMDY2Ru3atdWWAwBmZmYAgOrVqwMALl26hAsXLqBcuXK5zg0ICEDHjh3x5MkTXLp0qWgVIiIqRV27AtOnA82bA4XZCdfLC/j6a+DmTWDoUODLL+V3CxYAiYkl/rhE9IrQi5A5aNAgGBgYICIiItexI0eOAAC6desGU1PTfMvp1asXAGDnzp15Ho+OjkbVqlXh4+MD4L+xnnfu3MnzfA8PDwCq40aJiHTRwIGyK7xZMznbvDCMjIDu3WXr5urVwOnTQLVqwJgx7EonooLpRcj08PDA0KFDce7cuVxrYa5cuRLm5uYqi6KHh4fD398fCxYsUDl39OjRcHFxQVhYGK5du6ZybPv27YiLi8OsWbOU3eq1a9dGlSpVcPz48TzX0Dx27BjeeOMNlSWPiIh0Vf/+wOLFcuzl/v1Fu7Z+fWD5cuD8ecDBAWjcGHj3XaCAPTCI6HUm9ERSUpLw9fUV/v7+Ij4+XmRnZ4sFCxYIExMTERoaqnJuhw4dBABhZWWVq5zTp08La2trUbt2bXHz5k0hhBD//vuvqFy5spgwYUKu8//66y9haWkp6tWrJ65cuSKEECI1NVVMmTJF2NjYiBMnThSpDgAEAJGUlFSU6hMRaczffwtRsaIQS5cWv4xnz+T1NWoI0bixEDt2CJGdrblnJKKXowuZQy9aMgG5h3h4eDgCAgLg5+cHDw8P7Nu3DydOnMi1B3lQUBCsra3Rv3//XOXUqVMHR48ehbu7O3x8fODp6YmhQ4ciODgYc+bMyXV+o0aNcPz4cVSvXh2NGjWCi4sLqlevjnv37uHUqVPw8/MrsToTEZWEN98EDh0C5s0DPvoIyMzM//yffgLGjgV+/BH49185CcjMDBg0SLZsjhsHfPYZULMmsGwZ8P+RRkT0mlMIoWbxSdK45ORkWFlZAQCSkpJgaWmp5SciotfZkydA795yZ6Bff5Xd4C8SAjA1lYu6//sv8NdfwOPHQOfOckJR8+aAiYk87/BhOWHo7Flg/Hhg8GBAzR4aRFTCdCFz6E1LJhERaZaNDbBtm1ygvX594Pjx3Oekp8vtKAcPli2Z588DR48C3t4yUDo7yxnoR44Ab70F/P67LPPIEbneZnAwdxMiel0xZBIRvcYMDOTC64sWyZbJkBDZKpnD1FTuGtS//3/fu7rKrSrDw+VWlV5ewAcfALVrAwsXyt2Dfv1VtnpeuQJ4eMgF4bn8EdHrhSGTiIjQvr1sydyyBejUCXj48L9jP/8M3L8PTJ6sGkABoEIF4MMPZRf5zz8DJ0/KZY6GDQOysuQYzcOHgehoGTZnzJDd7UT06mPIJCIiAICLi1zaqH59oF49uY85IMdcbt0q18vMCY8vUijkhKIVK4DLl2VrZ4sWQOvWMoAuXiwD6MOHcheiadOA2NhSrR4RlTKGTCIiUjIykq2NGzbILvBx4+QWk3Z2MnTevSsn/SQkqC+jbFkZIqOiZBnffy+D5a5dwHffycXgnz2T3eyffJJ/WUSkvxgyiYgol7fekmHw0SPZsnnmjJwpvnWrHHvZoIGcbZ4fY2OgWzdg3z4gNFSGTA8PYP164PPPZZmPHwOennLryidPSqVqRFRKGDKJiChPtrbAypVy3/O2beVMcQCYPRuYNQto2VKOuSwMX18gLAzYuVN2n3t4AEuXyrB57JjcJ93TE/j2WyAlpeTqRESlhyGTiIjy1aOHHE8ZESG3k7x6Ve5pfuiQnE0+YACQlFS4smrWlMH177/lZCJPT2DuXODTT2X5p0/LAPrdd7Kbnoj0F0MmEREVyMkJ2LEDeP99ua7m/PlAlSoyLFpYAHXrAv/8o/761FS53NHvvwM3bshrFy8GLl4EypSRLZ0LF8qWzL175Tqb1avLGesZGaVXTyLSHIZMIiIqFIUCGDJELnW0bRvQrJmcCLR4sVxfs3NnuUD7i7PP168HzM2Bvn1lF3nz5oC7OzBqlByT+fnnwKVLgJWVHO/544/AggVy/Oe2bcAbbwDr1sntLIlIfzBkEhFRkbi5AXv2yC0pGzWS3d3t2wMnTgAHDgBNm8qZ5Tn8/OQ1Q4fK4BgVJVsrK1aUQfWdd+QkoFmzZMummRlQqxawcSOwfDmwerUMnn5+8r7cDJlIPzBkEhFRkRkYyDUzjx+XgbFhQzkTfedOGT4DAmRXtxBycfa//pJLGY0fD2Rmyi0nP/5Ydp137AhMnAjUqAFs2gTMnClDZ1qaXOZo2zbgt9+Azz4DPvpIrr+ZX9c8EekGhkwiIio2V1cZLEeOlAuvf/IJMHCgDJWrVslZ6bdvA5UqyfGbUVHyvLg4eb2ZGTBokAyNq1fL5Y7c3WXL5ZQpchxncrIMmydOyK0sBwwAAgPldpdXrmi1+kSUD4ZMIiJ6KQoF0K+fXJro5k3Ax0eO1Tx4UAbK+vVlq6atrWypfPtt2fW9f79qOQ0ayPU0jxyR+5x7e/838/zUKblDkLe33DUoMlK2ljZtCgwfDty7p5WqE1E+GDKJiEgjHB2BNWvkLPEhQ2SLZv/+cmmi1avlupo3b8rQuGKFPP7pp7L7/Hnu7nIJo/Pn5WQfLy85EejTT2UAPXlSzma3twcuXJA7DPn4yF2GuHsQke5gyCQiIo1q3Vp2czs5yQk8hw7Jbu4uXWTrY0iIXG/z5EnZIvnmm3nvHlSunGzJPHNGtpb6+AA//ADMmydbPNesAZo0kZOPIiMF7t+Pg4dHNIKD45CeztlBRNqmEILz9EpLcnIyrKysAABJSUmwtLTU8hMREZWs8+eBESNki+SPP8plioYNA+LjgSVL5JJFa9bIiT9TpgCjRwOGhnmX9eCBDJ3Ll8sF4qdOBY4dS8Do0Svx+PFCpKZeV55rbFwVffuOxrff9keZMnalU1kiHaILmYMhsxTpwi84EVFpE0IGw48/Bt57T3Z7b9sGTJokZ6LPnClD5+DBsrt7+XI59lKdR4/kYvDz5+9GSko3ZGam/P8+//11plAoIARgYGCBhQs3Y8SINiVbSSIdowuZg93lRERUohQKOf7y33+BJ09kF7qFhZwolJgot5qMjAT++EO2ZLZoIdfMTEvLuzx7e6Bhw91ISuqAjIxnEELgxfYS+XMBIZ5h5MgOaNlyN+7eLfGqEtFz2JJZinThXxVERNr2999ytx9HR7l25oMHcgkkZ2fZQmlpCXz4oRzXuXQp8NZbqtcnJCTAxcUFz549Q3YhtgEyMDCAkZE5rK3vYNQoO0yYILvtiV5lupA52JJJRESlqmFDueZlly5yAtAff8gZ6B06yJ8vXiy7zL/9FujTB/jgA9mdnmPlypVISUkpVMAEgOzsbGRkpGDMmFW4c0cu+r5sWe7tL4lIsxgyiYio1BkayglBZ84AMTFy5riTk+w2v3dP7lf+5InsUre1lV3sq1YB2dkCCxcuLPL9hABmz16A4cMFdu4E1q4FfH3lbkVEVDLYXV6KdKHpmohIFx05IkOno6PsMk9Olj+3tJRLFhkaylnpWVlxOHbMsdj3cXKKQ5s2ZfHVV3IJpYkT5baXISHyv0SvCl3IHGzJJCIirXvzTbm15LvvAs2bAxs2AH/+KRdz79hRdqFv3Qr07Jn0UvfZtespnJxky+n588CxY3KiUaNGckmkp081VCEiYsgkIiLdkNNa+e+/QFKSXMYoM1NOALK0lLPQk5NfbsZOr17WaN8eOHpUtmTWqwd4eMh7xMfL3YXWrZPd60T0cthdXop0oemaiEhfnDsHjB8vZ5/PmwdUqgRMmCCwa5cHMjNv5Fq2KD8KhQJVqlTB3LlX8eGHCjRvDgQHy3uMHQu4uMgu85QUOfPdyEh229erV4IVJCpBupA52JJJREQ6qVYtYPduuWbmiBEycH79tQKDBo0uVkvjmDFj0KWLAv/+C5QvL3cbun0bOHVKznR/+23g11+BXbvkjPYuXeR9Hz3SfN2IXgcMmUREpLMUCjkm89w5oE0bGQSF6A8LCwsoFIX7K8zAwAAWFhbo168fANn1/tVXwJ49cqvLVq2AZs3kGM2UlP+66f/9Vy4a7+Ulx4RyySOiomHIJCIinWdsLBdsv3ABsLCwg7HxZgAKGBjk/9eYQmEAhUKB3377DXZ2dirHatYEDh+WW102ayYXfp8/X7ZkLlkCdO4MDBoEHDwIbNkC+PvLWfBEVDgMmUREpDfKlJFjJ0+daoOGDXdACHMoFAooFAqV8+TPFRDCHB067ESNGq3zLM/AQO6Z/s8/MnD6+spWzL/+Avr2lTPdV62SIXPKFLnX+oABQGxsiVeVSO8xZBIRkd6pUgU4dKgNtm+/g0qVvoOpaRWV44aGVdCv33e4du0ufHxao359uX/69et5l+fiAmzbBsyYIbvnZ80C+vWTi8HfuSOXPCpTRnapu7jI8aLffy8DKRHljSGTiIj0Vvv2doiKGoNffrkKF5c4dOgQhaNH43Dw4FVcvz4GPXva4q23gCtXgOrV5T7oAwbIbve8dOsmlzY6cwZo0EBO+lm5EvjhB7m80gcfyH3VIyKAnTuB+vXlckhElBtDJhER6TUDA6B3bwWuXi2L5s0ro0uXsli1SoGNG4GZM+WuPu++Kyf4XL0qWyFbtwaCgoDLl3OXV7EisGmTnM3etCnw889ywfZz5+QySj4+snt9+3Zg+nSgZ09gyBDV/dWJiCGTiIheEWZmwEcfyVZLR0egTh05znLfPtn1/e67cgehli1l2KxfX85Wf/dd2TL54rJIffvKsZkrVwI9esjZ5V99BezYIScIdeokJwOdPw/Y28vwuno1F3InysGQSURErxQbG+CLL+R4yqQkOYv8wQPZBd6qlQyH770nWzNv3JBjMEeNkpN+1q4F0tL+K8vDAzhwAChXTm59efGiDK9//y1npOdcM3s28McfwKJFstyrV7VUeSIdwpBJRESvpAoVgIUL5bJDkZEybCoUcjxms2ZA27ZAnz5yV58zZ4BvvpFbSrq6ApMmyYXaAbl80qJFwOTJcrb5oUNyC8yJE2UL6Lp1six7e9ly2rUr0Lix3FEoI0OLL4BIyxgyiYjolValigyCu3bJru569WTL5JUrsuu8c2c5rrJCBXn8n3/ktpK+vkCvXnLW+ePHMpCuXy8nBx08KMuuXh0ID5cLxfv5AcuXy12CTp6U4bZ+feD4ce3Wn0hbGDKJiOi1UKsW8PvvctH1RYuAgAAZLC9fBho1Atq1kwHy0SM59vLGDdlyuWQJULWq7DofPFiukXn27H/lGhjISUIREfLcbt0AU1O5tuann8qfT5wIPHumtaoTaQVDJhERvVaaNJEtkSEhsou8USPA3V22bLZoIcdsvvOODJJDh8pgGhsr91Hftw+IiZFjOF9UvbrsSvf2ljPQd+2Sk4rOngXi4mTIDQ8v/foSaYtCCM6DKy3JycmwsrICACQlJcHS0lLLT0RE9HoTQobBmTPl7PHPP5dBc80aGUJtbeVYzM6dZYtlYR07JpdI6t5dTkIyMZEBdfBgoH17OV7T2rrEqkWkE5mDLZlERPTaUihk6Dt2TAbM6dPlpCBnZ7ku5iefAHPmyFbIZctUZ57nx99fjsuMigIaNgRu3pTh9exZGWxr1ZKhk+hVxpBJRESvPYUC6NBBBsOpU4GPP5YzxE1N5VqZP/4IbN0qx2Z+8w2QmFhwmWXKABs2AIMGyeWP9uyRrZeLF8u1N4cMkd3uT5+WfP2ItIEhk4iI6P8UCqBLFxk2J06Ui7u/9ZacDBQWJoPi5csybA4ZIs8rqLzhw+UOQoMHA99+K1symzaVyyplZf237ibRq4Yhk4iI6AUKhZz8ExkpWzW/+UZ2cUdGym0mL10CatSQSxy99ZYMkfmtidmwoeyS37BB7n+ekSEXjf/hBznTvWdP2YJa2O54In3AkElERKSGgYHcEejwYdnN/csvgKcn8NtvwOjRslVz6lTZne7mJpcyOnMm77IqVJCz2u/fl0EzZ9pt27YyvEZFyW71K1dKrXpEJYohk4iIqBCaNgX27gV+/RXYuVOum7l4sdz/fO9e2eVtZyfXxfT1Bb7/Hnj4ULUMCwvZmnnpkmwdzVG2rCx39Gg5FvSnn7gHOuk/hkwiIqIiqF9fLrS+bZts4axSRXapm5rK2elXr8rlj06dAry85KLvn30GnDgBZGcD5uZyTOeDB6rlKhTA++/LsPrLL3IJpCdPtFJFIo1gyCQiIioGHx/Z+nj8OJCaCtSuLScDXbwoWz2XLZNd43PnAunpsovcyUku/n72rNxVKC9Vq8pF3StUkFtgFjS5iEhXcTH2UqQLC6MSEVHJSEiQk4K+/15OEpo8WXZ9KxT/nXPvngyNvr5AxYoFl/n773LXodmzgQEDSurJ6VWkC5mDIbMU6cIvOBERlayMDNnC+c03gKUlMGIE0KOH7CYvjmvX5DjPBg3kTHQTE80+L72adCFz6FV3eXp6OoKDg+Hp6YmqVauiadOmiIiIeKkyHz9+jJCQEHTt2hVDhw7FzJkzkfHcOhRz586FQqHI9zMqr01siYjotWRsDPTrJ3cM+uILOXbTzQ2YMAGIji56edWqAUePykXbW7WS+6AT6QO9CZlpaWlo27YtVq9ejT179uD69esYNWoUWrZsidDQ0GKVuW7dOnh6euLRo0dYs2YNfv75Z8ycORPGxsbKc3755ZcCy+nYsWOx7k9ERK8uhQJo3RrYvBk4fRowM5PbTXbuDOzYIRdiLyxzc9k62qKFXObo5s2Se24iTdGbkDl58mSEh4dj+fLlcHV1BQB0794dgYGBGDBgAKKioopU3rRp0zBo0CCsXr0aX375pbJJ+XmHDh1CWloatmzZgqioKMTGxqp8pk+fjjJlyqBFixYaqSMREb2anJ2BL7+U4bBnT+Drr+Ws9C++AO7cKVwZCgXw6adyJ6JmzYDr10v0kYleml6EzOjoaCxatAheXl5o0KCByrG+ffsiJSUFU6dOLXR5wcHBmD17NlavXo02bdqoPW/Hjh04evQounTpgsqVK8PBwUHls3v3brzzzjsqLZ9ERETqmJkBffoAERHArl1yu0pfX9kNvnYtkJJScBlDh8qlkjp25L7npNv0ImRu2LABmZmZaNiwYa5j/v7+AICwsDDEx8cXWNbu3bsxbdo09OzZE4GBgfme++mnn8LR0THPY7du3cLx48fRvXv3QtSAiIhIlZcXMG8ecPs2MGqU7FZ3dZV7nP/5p1z2SJ2BA4E2bYC+fbloO+kuvQiZO3bsAABUqVIl1zF7e3s4OzsjPT0dhw8fzrecjIwMjB07FkIIzJgxo8D7muczFTA0NBT29vbsKiciopdiYgJ06SK3qrx4EahTBwgOlksc9ewJrF6d92Sfb7+VC7dzIhDpKr0ImadPnwYAuLi45Hnczs4OABAZGZlvORs3bsTly5fRoEEDXL16FUFBQahXrx7c3NzQp08f3Lhxo9DPFBoaiq5du7KrnIiINMbRUbZq7t8v9zDv3FluYenpKbvVJ0wAli+XOw7t3Stnst+/r+2nJsqbkbYfoCCpqalISkoC8F+YfJGtrS0AIK6Af87lzEKPjY1FUlISli1bBkNDQ8yfPx+TJk3C7t27ERERAS8vr3zLyekq/+yzzwp8/rS0NKSlpQGQa1YREREVRtmycvxmnz5AZqacoX7ggFzOKCFBjsccMUIucUSki3Q+ZD4/ztLCwiLPcwwMZINsampqvmUdPHgQAJTrYuaYOHEizpw5g7Vr12LAgAE4fvx4vuVs2rQJZcqUQfPmzQt8/tmzZxcqjBIREaljZCT3TK9fX9tPQlR4Ot9dbvLc1gbqNidK///oaHt7e7XlJCcnIyEhAQDg7Oyc6/iIESMAACdOnMD58+fzfabQ0NBCzyqfOnUqEhMTkZiYiJiYmALPJyIiInoV6HzItLe3VwZNdd3NOeHRwcFBbTlPnjxR/tjGxibX8YYNGyq74y9evKi2nNu3b+PYsWOFnlVuamoKGxsb5YeIiIjodaDzIdPQ0FA5RlJdS+CDBw8AAHXq1FFbjoODAxQKBQDVwPm8nIlF2dnZasvJ6SrnrHIiIiIi9XQ+ZAJQLpieVzd2XFwcEhMTYWlpicaNG6stw9jYGLVr11ZbDgCYmZkBAKpXr662nJyuciMjnR/OSkRERKQ1ehEyBw0aBAMDA0REROQ6duTIEQBAt27dYGpqmm85vXr1AgDs3Lkzz+PR0dGoWrUqfHx88jx+584dHD16FD169CjK4xMRERG9dvQiZHp4eGDo0KE4d+5crrUwV65cCXNzc5XF1cPDw+Hv748FCxaonDt69Gi4uLggLCwM165dUzm2fft2xMXFYdasWcpu9Rdt2rQJ9vb2hZpVTkRERPQ6Uwh1U7Z1THJyMpo2bQojIyPs3LkTZcqUwffff48JEyZg7dq1KltEduzYETt27ICVlRWevrCxa2RkJJo0aQJ3d3f8/vvvcHV1xfnz59GxY0cEBgZizpw5ap/hrbfegpeXF5YsWVLsOlhZWQEAkpKSYGlpWaxyiIiIiPKjC5lDL1oyAcDS0hLh4eEICAiAn58fPDw8sG/fPpw4cSLXHuRBQUGwtrZG//79c5VTp04dHD16FO7u7vDx8YGnpyeGDh2K4ODgfAPm3bt3ceTIEe5VTkRERFQIetOS+SrQhX9VEBER0atPFzKH3rRkEhEREZH+YMgkIiIiIo1jyCQiIiIijWPIJCIiIiKNY8gkIiIiIo1jyCQiIiIijWPIJCIiIiKNY8gkIiIiIo1jyCQiIiIijWPIJCIiIiKNY8gkIiIiIo1jyCQiIiIijWPIJCIiIiKNY8gkIiIiIo1jyCQiIiIijWPIJCIiIiKNY8gkIiIiIo1jyCQiIiIijWPIJCIiIiKNY8gkIiIiIo1jyCQiIiIijWPIJCIiIiKNY8gkIiIiIo1jyCQiIiIijWPIJCIiIiKNY8gkIiIiIo1jyCQiIiIijWPIJCIiIiKNY8gkIiIiIo1jyCQiIiIijWPIJCIiIiKNY8gkIiIiIo1jyCQiIiIijWPIJCIiIiKNY8gkIiIiIo1jyCQiIiIijWPIJCIiIiKNY8gkIiIiIo1jyCQiIiIijWPIJCIiIiKNY8gkIiIiIo1jyCQiIiIijWPIJCIiIiKNY8gkIiIiIo1jyCQiIiIijWPIJCIiIiKNY8gkIiIiIo1jyCQiIiIijWPIJCIiIiKNM9L2AxRFeno6QkJCsHz5cmRmZsLFxQVffPEFmjRpUuwyHz9+jOXLlyMiIgLlypWDk5MTPv74YxgbG6u95uDBg1i7di3i4+Ph7u6ODh064O233y72MxARERG9avQmZKalpaFdu3Z48OAB9uzZA1dXV4SGhqJly5ZYu3YtunfvXuQy161bhw8//BBDhw7FmjVrYGVlle/5Dx8+xODBgxEdHY0ff/wRDRs2LG51iIiIiF5pehMyJ0+ejPDwcBw7dgyurq4AgO7duyMsLAwDBgyAn58f3N3dC13etGnTMG/ePGzZsgVt2rQp8PwrV66gdevWqFmzJo4ePQoLC4ti14WIiIjoVacXYzKjo6OxaNEieHl5oUGDBirH+vbti5SUFEydOrXQ5QUHB2P27NlYvXp1oQLm/fv30aZNGzg4OGDTpk0MmEREREQF0IuQuWHDBmRmZubZPe3v7w8ACAsLQ3x8fIFl7d69G9OmTUPPnj0RGBhY4PlCCAQGBuLWrVtYuXIlzMzMil4BIiIioteMXoTMHTt2AACqVKmS65i9vT2cnZ2Rnp6Ow4cP51tORkYGxo4dCyEEZsyYUah7r1q1CocPH0b37t3h7e1d9IcnIiIieg3pRcg8ffo0AMDFxSXP43Z2dgCAyMjIfMvZuHEjLl++jAYNGuDq1asICgpCvXr14Obmhj59+uDGjRu5rvnyyy8BAC1btsQnn3yCtm3bwtXVFX5+fvjxxx8hhCh+xYiIiIheUTofMlNTU5GUlATgvzD5IltbWwBAXFxcvmWFhoYCAGJjY5GUlIRly5bh6NGjGDVqFNatW4cGDRrgwoULyvMjIyNx7do1KBQKHDlyBP369cMff/yBP//8E2lpaRg+fDiGDx+e7z3T0tLw5MkT5YeIiIjodaDzIfP5cZbqJtwYGMhqpKam5lvWwYMHAQAhISHo3bs3zM3NYWJigokTJ6JPnz6Ij4/HgAEDcp1fu3Zt/PLLL6hevToAoEaNGti8eTMMDAzw008/YefOnWrvOXv2bNja2sLW1hZOTk4FV5iIiIjoFaDzIdPExET5Y3Vd0+np6QDk+Ex1kpOTkZCQAABwdnbOdXzEiBEAgBMnTuD8+fMAgDt37qg9v3r16mjRogUAYPny5WrvO3XqVCQmJiIxMRExMTFqzyMiIiJ6leh8yLS3t1cGzeTk5DzPyQmPDg4Oast5vqvaxsYm1/GGDRsqu+MvXryock1e5wNAhw4dVM7Pi6mpKWxsbJQfIiIioteBzodMQ0NDeHl5AYDalsAHDx4AAOrUqaO2HAcHBygUCgBQOzYyZ2JRdnY2AMDR0bFI5xMRERGRpPMhE4BywfScbuznxcXFITExEZaWlmjcuLHaMoyNjVG7dm215QBQroGZM/bSz8+vSOcTERERkaQXIXPQoEEwMDBARERErmNHjhwBAHTr1g2mpqb5ltOrVy8AUDtRJzo6GlWrVoWPjw8AoFWrVrC3t8fNmzdVZp3niIqKAgC8++67ha8MERER0WtAL0Kmh4cHhg4dinPnzuVaC3PlypUwNzdXWVw9PDwc/v7+WLBggcq5o0ePhouLC8LCwnDt2jWVY9u3b0dcXBxmzZql7Fa3tLRUlvvNN9/keq5Vq1ahVq1a6N27tyaqSURERPTqEHoiKSlJ+Pr6Cn9/fxEfHy+ys7PFggULhImJiQgNDVU5t0OHDgKAsLKyylXO6dOnhbW1tahdu7a4efOmEEKIf//9V1SuXFlMmDAhz3v37dtXABDz588X2dnZIiMjQ0ydOlWUL19eXLt2rUh1ACAAiKSkpCLUnoiIiKjwdCFz6EVLJiBbFcPDwxEQEAA/Pz94eHhg3759OHHiRK49yIOCgmBtbY3+/fvnKqdOnTo4evQo3N3d4ePjA09PTwwdOhTBwcGYM2dOnvdesWIFvv32W3z//feoUKECfHx88OjRI5w5cwZVq1YtkfoSERER6TOFENwXsbQkJyfDysoKAJCUlARLS0stPxERERG9inQhc+hNSyYRERER6Q+GTCIiIiLSOIZMIiIiItI4hkwiIiIi0jiGTCIiIiLSOIZMIiIiItI4hkwiIiIi0jiGTCIiIiLSOIZMIiIiItI4hkwiIiIi0jiGTCIiIiLSOIZMIiIiItI4hkwiIiIi0jiGTCIiIiLSOIZMIiIiItI4hkwiIiIi0jiGTCIiIiLSOIZMIiIiItI4hkwiIiIi0jiGTCIiIiLSOIZMIiIiItI4hkwiIiIi0jiGTCIiIiLSOIZMIiIiItI4hkwiIiIi0jiGTCIiIiLSOIZMIiIiItI4hkwiIiIi0jiGTCIiIiLSOIZMIiIiItI4hkwiIiIi0jiGTCIiIiLSOIZMIiIiItI4hkwiIiIi0jiGTCIiIiLSOIZMIiIiItI4hkwiIiIi0jiGTCIiIiLSOIZMIiIiItI4hkwiIiIi0jiGTCIiIiLSOIZMIiIiItI4hkwiIiIi0jiGTCIiIiLSOIZMIiIiItI4hkwiIiIi0jiGTCIiIiLSOIZMIiIiItI4hkwiIiIi0jiGTCIiIiLSOL0Kmenp6QgODoanpyeqVq2Kpk2bIiIi4qXKfPz4MUJCQtC1a1cMHToUM2fOREZGRq7z7t+/DzMzMygUCpWPi4tLnucTERERvc6MtP0AhZWWloZ27drhwYMH2LNnD1xdXREaGoqWLVti7dq16N69e5HLXLduHT788EMMHToUa9asgZWVldpzQ0JCkJaWluv7MWPGwNjYuMj3JiIiInqV6U3InDx5MsLDw3Hs2DG4uroCALp3746wsDAMGDAAfn5+cHd3L3R506ZNw7x587Blyxa0adMm33MfP36MdevWITIyEqampirHKleuXOS6EBEREb3qFEIIoe2HKEh0dDQ8PDxQvXp1nD9/XuXYrl270L59e/Ts2RPr168vVHnBwcGYOnUqQkNDERgYWOD5X3zxBZKTkxEcHFys58+RnJysbC1NSkqCpaXlS5VHRERElBddyBx6MSZzw4YNyMzMRMOGDXMd8/f3BwCEhYUhPj6+wLJ2796NadOmoWfPnoUKmCkpKViwYAGys7Nx+PBhZGdnF70CRERERK8ZvQiZO3bsAABUqVIl1zF7e3s4OzsjPT0dhw8fzrecjIwMjB07FkIIzJgxo1D3/vnnnxEXF4c5c+agUaNGqFy5MhYtWoSsrKyiV4SIiIjoNaEXYzJPnz4NAHBxccnzuJ2dHe7evYvIyEh07txZbTkbN27E5cuX0aBBA1y9ehWff/45Ll++jPj4eDRq1AhffPFFriD7zz//oGbNmrhx4wZSUlJw+/ZtjBo1Cps2bcJvv/2GMmXKFLoez49MSE5OLvR1REREREXxfM7Q2shIoeOePXsmAAgAYtu2bXme07BhQwFAjB49Ot+yunTpIgAId3d3sXbtWpGSkiLS0tLEN998IwCIsmXLivPnz+d5bVpamvjzzz+Fn5+f8nmaNWsmsrKy8r1namqqSExMFImJieLatWvKa/nhhx9++OGHH35K4/PgwYPChS4N0/mQeefOHeVL2rt3b57nNGrUSAAQQ4YMybcsOzs7AUCEhYXlOtanTx8BQNSvXz/fMrKyssTYsWOVz7R69ep8z58xY4bWf3Pxww8//PDDDz+v7+fWrVv5ZpWSovPd5SYmJsofCzXNvenp6QDk+Ex1kpOTkZCQAABwdnbOdXzEiBFYu3YtTpw4gfPnz8Pb2zvPcgwMDPDdd9/h1q1bCAsLw/r16/Hee++pve/UqVPx0UcfAQCys7MRHR2NunXr4u7du7C1tVV7Hf3nyZMncHJyQkxMDGxsbLT9OHqD763o+M6Kju+sePjeio7vrGiEEHjw4AGqVasGa2trrTyDzodMe3t7mJiYID09Xe04xpzw6ODgoLacJ0+eKH+c12/Ohg0bws7ODgkJCbh48aLakJkjODgYW7ZswfXr1/M9z9TUVGVtzZwxn1ZWVlzCqJByJllZWlrynRUB31vR8Z0VHd9Z8fC9FR3fWdHlrIhjYKCded46P7vc0NAQXl5eAICYmJg8z3nw4AEAoE6dOmrLcXBwgEKhAKAaOJ+XM7GoMMsUVa9eHa6urvnuEkRERET0utL5kAlAuSPPiwuxA0BcXBwSExNhaWmJxo0bqy3D2NgYtWvXVlsOAJiZmQGQAbIwKlasiICAgEKdS0RERPQ60YuQOWjQIBgYGCAiIiLXsSNHjgAAunXrlmvLxxf16tULALBz5848j0dHR6Nq1arw8fEp8JkyMzNx/fp1DB8+vMBzn2dqaooZM2YU+Kz0H76z4uF7Kzq+s6LjOysevrei4zsrOm2/M73YVhIAhg8fjh9//BGnT59W6RYPDAzEzp078e+//yrHO4aHh2PKlCno06cPxowZozw3OTkZNWrUwP3793Hx4kVUq1ZNeWz79u3o1KkT1q9fj549eyq/j4uLy3Os57x58/Ds2TNMmzatBGpLREREpN/0oiUTAL799lv4+vpi2LBhePToEYQQWLhwIX7//XesWrVKZRH1uXPn4vjx4/j4449VyrC0tMTvv/8Oc3NzdOvWDbdu3QIgu89Hjx6NCRMmqATMkJAQODo6ol27drh06RIAIC0tDQsWLEBmZiYDJhEREZEaOj+7PIelpSXCw8Mxffp0+Pn5wcDAADVr1sSJEyeUYy1zBAUFISIiAv369ctVTp06dXD06FFMmzYNPj4+KFeuHBwcHBAcHKwSMAGge/fu2L9/Pw4fPoy6deuifv36aNiwIQYMGIAaNWqUaH2JiIiI9JnedJcTERERkf7Qm+5yXZCeno7g4GB4enqiatWqaNq0aZ6TkfKTlJSESZMmwd3dHSYmJnBxccGwYcNw7969PM+fO3cuFApFvp9Ro0ZponolQhvvDABu3ryJ999/Hy4uLnB1dUWlSpUwcOBA3L59+2WrVCq09d5u3bqFgQMHokqVKqhYsSK8vb2xYMEC5fp0ukwT7ywrKwsLFiyAt7c3zM3N4ebmhqlTpyItLS3f6zZt2oT69eujSpUqqF27NpYuXfoyVSk12nxnAHD27FkEBQVh0KBBxa2CVmjrvYWHh6NFixawtraGpaUlGjZsiPXr179sdUqFtt7ZpUuX8M4776B8+fKwsrLC22+/rZwwrOu0/f9njjt37qBMmTIYMGBAEWsA6Py2kroiNTVVvP3228LLy0vcvHlTCCHExo0bhbGxsdi4cWOhynj69KmoW7euACAMDQ2FQqFQbvlUoUIFceXKlVzXvPHGGwVuF7Vr1y6N1lVTtPXOrly5IsqWLSs6d+4sYmNjhRBCPHjwQDRv3lyUK1dOXL9+XXOVLAHaem8nT54UdnZ2YsKECSItLU0IIcT69euFsbGx6N69u8jKytJcJTVME+9MCCHef/99YWhoKCpVqiSMjY2V76xXr15qr5k6daqwsrISBw4cEEIIcfHiReHo6ChGjx79cpUqYdp8Z8eOHROBgYHK35f9+/d/2eqUGm29t9WrVwsDAwMBQBgZGan8HfDRRx9ppG4lRVvv7Ny5c8La2lqUKVNGlCtXTnm+mZmZOH36tCaqVmK0+f/n87Kzs0WrVq2K/f8pQ2Yh5exXfuzYMZXvg4KChIWFhbhx40aBZUyaNEnUqVNH7N+/X6SlpYmnT5+Kb775RvkHxptvvqly/l9//SWqVKkitmzZIqKiokRsbKzKZ/r06aJMmTIiPT1do3XVFG28MyGE6NixoyhTpoxISkpS+f7WrVsCgOjRo8fLVayEaeO9JScnCzc3N9GgQYNcZX355ZcCgJgzZ87LVawEaeKdrV+/XnTv3l3cu3dPCCFEUlKSGDhwoPIP5TNnzuS6JiwsTAAQX3/9tcr3P/30kwAgNmzY8BK1KlnaemdCCHHjxg2RlZUl+vfvr3chUxvv7eHDh8LW1lZMnz5dec3JkyeFn5+f8prdu3drqIaap413lpWVJRo1aiQ2bdoksrOzhRBCRERECEdHRwFAvPPOOxqqXcnQ5v+fz/v++++FlZUVQ2ZJioqKEkZGRsLLyyvXsZ07dwoAomfPnvmWkZmZKQICAsTjx49zHZs+fbryF/35VrYpU6aIhw8fqi2zQYMGYuDAgYWvSCnS1jsTQggLC4s8w5IQQjg4OIg33nij8BUpZdp6bzmhaObMmbmuefz4sTA2NhZ2dnbi6dOnRa9UCdPEOxNCiG+++SZXa21GRoaoUqWKAJCr9SArK0t4eHgIhUIhHjx4oHLs6dOnwtDQUDg7O4vMzMxi1KpkaeudvWjx4sV6FTK19d6+++478f333+cq5+HDh8Le3l4AEH379i1ibUqHtt7ZkSNHlL0Lz/vll18EgDyfR1foyv+fV65cEa6urmL27NkMmSUpODhYABCDBw/OdSw+Pl4AECYmJiIuLk5tGXfu3BFhYWF5HktISFD+xX/06FHl9ykpKWrLu3nzpk53lWvrnQkhhKOjo7CyshKJiYkq32dlZQkLCwsRFBRU9AqVEm29tx49eggA4ueff87zuoCAAAFAhIaGFq1CpUAT7yw/3bt3FwDEpUuXVL4/evSoACCqVauW53W1a9cWAMS2bduKdd+SpK139qJly5bpVcjU1ntbvHix2uEq48aNEwBE27Zti3XPkqYrv9dynD9/XgAQvXv3Ltb9SoMuvLPMzEzx5ptvik2bNonly5cX+/9TTvwphB07dgCAylqcOezt7eHs7Iz09HQcPnxYbRnOzs7o2rVrnsdsbW1Rrlw5AICrq6vye3Nzc7XlhYaGwt7eHi1atChMFUqdtt4ZAHTu3BlJSUkYOXKkyve7d++GoaEhZsyYUZSqlCptvbdHjx4BABITE/O8zs3NDQBw4sSJgitRyjTxzvJz//599O7dG56enoW+LwDUqlULgJysoWu09c5epFAoilW+tmjrvQ0fPhwGBnn/de3h4QHgv/9HdY2u/F57/nxTU1NMmTKlWPcrDbrwzr7++mtUq1YN3bp1K9Y9cjBkFsLp06cBAC4uLnket7OzAwBERkYWq/zMzEwkJCSgQYMGqFixYqGuCQ0NRdeuXWFsbFyse5Y0bb6zL7/8EpUqVcKaNWswZswYZGdn4+HDh5g9ezb27dtX6D+MtEFb783Z2RmA+hAp/r/SWVxcXLHuW5JK8p2dOnUKGRkZ+OGHH0r1viVNW+9M3+nie8v5f7JLly5Fvmdp0LV3tnjxYvzyyy/KfwTqIm2/szNnzmD58uVYuHBhkct/EUNmAVJTU5GUlATgv1/YF9na2gIo/l/Af/31F9LT0zFx4sRCnX/r1i0cP34cPXr0KNb9Spq231mFChUQHh6OypUrY+HChQgMDMTkyZOxdetW1K9fv1j3Kw3afG/vvvsuAGDbtm3KnbCed/fuXQCAiYlJse5bUkrynf3xxx/o0KEDqlevjuTk5FzHY2NjS+S+JU2b70yf6ep727t3L2rVqoU2bdoU6brSoEvv7PHjx3j//fdx+PBhmJmZFelepUnb7yw9PR39+/fHzz//rLzPy2DILEB8fLzyxxYWFnmek9ONkZqaWqx7LFy4EC1btkRgYGChzt+0aRPKlCmD5s2bF+t+JU0X3lnVqlWxdu1ajBkzBuHh4VixYgVmzZqF7OzsYt2vNGjzvXXu3Bl9+/ZFamoqOnbsiPPnzwMAoqOjMXnyZGULp651yZXEO7tw4QKCgoLQqVMn3L9/H6tWrUK9evVw8eLFPO9dUr9WJUWb70yf6eJ7O3PmDA4dOoRFixap7U7XJl15Z9999x38/PywYsUK3L9/H4GBgfjwww8LX5FSpO139umnn6J58+Z4++23i/H0eTyrRkp5hT3fciPUbI6Unp4OQI6VKKoDBw7g0KFDWLFiRaGvCQ0NxTvvvKOzXeW68M727t2LXbt2Yf78+Th06BCcnJwwd+5c9O7dW2eDprbf28qVK7F48WKYmZmhTZs2aN68OX744Qe88cYbyvu++eabRb5vSSqJd+bl5YVff/0VDx8+xJdffgkzMzPcv38fgwcPzvPeJfFrVZK0+c70mS6+t3HjxmHy5Mlo3Lhxoc4vbbryzj788ENcu3YNERERyj/D5s+fjz179hS2KqVGm+/s77//xs6dO/HVV18V8+lzY8gsgL29vfIXXV3zckJCAgDAwcGhSGU/fvwYI0aMwG+//aYcE1eQ27dv49ixY+jevXuR7lWatP3ODh8+jMDAQIwfPx4A4O3tjYMHD6J8+fLYsGEDvv766yLds7Ro+70pFAoMHz4cx48fx507d7B//358/fXXyvFBTk5OOveXWUm+szJlyuDjjz9GaGgoAPkH8PNDCSpUqFAi9y1p2nxn+kzX3tv8+fNhZWWFL774okj3Kk269M4UCgUaN26MgwcPIiAgAADw66+/FumepUFb7yw5ORmDBw/GihUrNDqcgCGzAIaGhvDy8gIAxMTE5HnOgwcPAAB16tQpdLlZWVno168fvvjiCzRq1KjQ1+V0levqrHJAu+8sOzsbQ4cOha+vr8p4lmrVqmHLli0wMjJCcHCwznVhArr3ew2QY35WrVoFABg/frzOdcmV1Dt7XseOHdGgQYNc96hdu3aJ3rekaPOd6TNdem8HDx5EWFgY1q9fD0NDw2LdqzTo0jvLYWxsjE8//bTQ55c2bb2z3377DRcvXoSvr2+uravff/99ALK3S6FQoHLlyoW+l279jaGjcgZU54xTe15cXBwSExNhaWlZpFae4cOHo0uXLkVeHiCnq9zIyKhI15U2bb2zS5cu4cKFC8plep4XEBCAjh074smTJ7h06VKh71uadOn3GgB88sknSEhIQK1atTB69OgiX18aSuKdvSgnnD8/Iz+/+wLAtWvXAADt2rUr9n1Lirbemb7Thfd2/vx5TJ8+HWFhYWrH7OkSXXhnL3t+adPGO7OysoKnp2een5xeGxsbG+U+6oXFkFkIgwYNgoGBQZ4b0x85cgQA0K1bN5iamhaqvPHjx8PDwyPPMSTx8fF48uRJntfduXMHR48e1dlZ5c/T1jvLGaty586dPMvJWVNO12ZJ59CV32uAXKvt559/hr29PTZv3qyzY4A1/c7ykpiYCB8fH5WJTy1btoS7uzsuXryonGmeIyEhARcvXoS7u7uya06XaOud6Tttv7crV65g+PDh2LhxI8qUKZPreFRUVLHvW1K0/c7UnQ/o7rJP2nhn77zzDi5dupTnZ/bs2Srn7Nu3r/A3KvLy7a+pYcOGCQDi9OnTKt9369ZNmJubq2zRt3//ftGgQQMxf/78XOVMmDBBfP7553ne4+zZs6Jx48a59tzOMW/ePFG2bFmRkZFR/IqUIm28s6ysLFGlShVhYmKSa7tJIYRo0qSJTm8rKYRu/F7bs2ePsLS0FOXLl8/1HLpIU+8sL/Hx8aJixYpi7969uY6tX79eABDz5s1T+X7hwoUCgFi3bl2R61JatPXOnrdkyRIBQLz33ntFfn5t0dZ7u/y/9u4/pO7qj+P4616XrvQ69a5aOVupYCPHyhurtJYR2aDAWMVtFFmbtZH9oFq2CmpFraxGZSsUluyPRdYfsQhaRT/AwfZHF7wWsdStaOZCQ1cXmTr1vr9/DC+73+uWy3O99v0+H3CRz7mfc+95Hz4XX57P9fPp7LSKigr79ddfE547evSobd261Z5//vnTK2aWzIVj7UQvvfSSVVZWxu5pPhfNpTmbyR1/CJnTNDQ0ZIFAwK644gobGBiwaDRqjY2Nlp6ennCrvZtuuskkWVZWVqwtGo3aAw88YB6Px/x+f9wjLy/PzjzzTJNkd95550nHUF5ePuVtpuaqVM3Znj17LDMz08rKyqyrq8vMzEZGRmzTpk2WnZ1t3333XfKLn4FUHmvt7e22YcMGS0tLs1WrVtnhw4eTXq8LM52zP/74wwoLC620tNRaWlpsZGTEzMwOHDhg119//SnD4vr1683v91tHR4eZmbW1tVl2drY9+uijSajUnVTO2aTa2lqTZMuWLbPh4WG3BSZJKuato6PDzj33XMvMzEz4TOfk5JjX6zVJ1t3dndzi/6FUzNnLL79sfr/f7rnnHtu/f7+ZHb9v97vvvmtVVVU2ODiYxIpnbi58PicRMmdJJBKxRx55xC666CIrKiqy6urq2C+WE+3cudN8Pp/V1dXF2urr62P3jD7V47PPPpvyvX/77TfzeDz2xRdfJK2+ZEjVnP344492xx132DnnnGP5+fl2wQUXWE1NjR04cCCp9boy2/O2Y8cO8/l8tnjxYgsGg/+648xsZnM2NjZm9957r/n9fvN6vXb22WdbVVWVbdy40Q4dOnTK941Go/b222/b0qVLrbCw0K666irbtWuX8/qSIVVz9uabb1peXl7c8ZiZmWl333238xqTYTbnrbe313Jzc//287xixYqk1jxTs32sffPNN1ZaWmoZGRmWnp5uy5cvt2AwaK2trXN6BfNEqfp8/reZhEyP2UkuxAQAAAD8Q/zjDwAAAJwjZAIAAMA5QiYAAACcI2QCAADAOUImAAAAnCNkAgAAwDlCJgAAAJwjZAIAAMA5QiYAAACcI2QCAADAOUImAAAAnCNkAgAAwDlCJgAk0eDgoBoaGlRZWanzzjtPGRkZWrhwoQKBgOrr69XV1ZXqIQJAUnjMzFI9CAD4X/Tee+/pscceUyQSkSR5PB7l5ORoaGhIY2NjkqS0tDQ9+OCDev311zVv3rxUDhcAnGIlEwCS4IUXXlBtba0ikYiuvvpq7d69W8PDwxocHNTo6KhCoZDWrl2raDSqt956SzfffLPGx8dTPWwAcIaVTABw7NNPP1V1dbXMTHV1dWpsbJTXO/Xf9B999JHWrFmjaDSqjRs36rXXXpvl0QJAchAyAcChiYkJXXLJJers7FRFRYXa2tpOGjAnPfPMM9qyZYvmzZunzs5OFRYWztJoASB5OF0OAA59++236uzslCRt3rz5bwOmJNXX1yszM1Pj4+Pavn17socIALOCkAkADn399deSpKysLFVWVk6rz4IFC3TNNdfE9QeAfztCJgA4dPDgQUlScXHxaf23eElJiSTpl19+Scq4AGC2ETIBwKE///xT0vGVzNMxuf9ff/3lekgAkBKETABwKC8vT5J05MiR0+o3uf/ChQvj2icmJtTQ0KDi4mJlZGSoqKhIL774Ipc7AjDnETIBwKGCggJJUldXl0ZGRqbd7/vvv5ckLV68OK79oYce0qZNmxQIBPTOO+/ohhtu0LPPPqv169e7GzQAJAEhEwAcWrVqlSRpbGxMu3fvnlaf/v5+7du3T5J04403xtp/+OEHNTU1KRgM6sMPP1Rtba2ampr0xBNPqKWlRaFQyH0BAOAIIRMAHLruuutiq5nTvbD6G2+8oYmJCXk8Hq1ZsybW3traKjPTww8/HLf/5HZra2tc+7p163TWWWepp6dHNTU18vv9ys3N1YYNGzQ+Pq6hoSE9/vjjKigokM/n0y233KKBgYGZlAsAJ0XIBACHvF6vGhoaJEn79u3TK6+8csr99+zZo61bt0qS1q5dq6VLl8aeC4VC8nq9uvzyy+P65OfnKz8/P2ElMxwOa8GCBVq5cqWysrK0ZcsWBQIBNTc3q6GhQRUVFerp6dHTTz+t2267TZ988omee+45F2UDQALu+AMASbBu3Tq1tLTI6/Xq448/VnV1dcI+Bw8eVHl5ufr7+1VaWqq9e/fK5/PFnl+2bJn6+/vV19eX0HfFihU6cuSIuru7JUnj4+PKysrSxMSEvvrqK1177bWSpEgkotzcXEWjUW3btk11dXWx1ygpKZHP5+O0O4CkYCUTAJKgqalJq1evVjQa1erVq/Xkk09qbGws9vz777+vsrIy9ff3q6SkRJ9//nlcwJSko0ePKiMjY8rXnz9/voaHh2Pb+/fv1+joqO67775YwJQkn8+nM844Q1deeWVcwJSk7OxspaWluSgXABIQMgHAoUWLFmnRokUqKChQW1ubJCkajerVV1/VBx98IEk6dOiQ7rrrLkUiEUnS77//rkAgEOu7d+9eSceD5Ojo6JTvMzIyovnz58e2w+GwJOn222+P26+7u1ujo6O69dZb49rNTF1dXbr44otnXjQATGH6t6MAAPytqU5tT4pGo3E/J0UikVjglKRjx45JOn45o59++knHjh1Tenp6XJ/Dhw+rqKgoth0Oh+XxeBK+v9ne3i7p+On1E/3888+KRCK67LLLplsaAJwWQiYAODSdr7lfeOGF09ovEAjoyy+/VCgUUnl5eay9t7dXvb29CgaDsbZwOKzi4uKEU+6T4fPSSy9NaJeU0A4ArnC6HADmqGAwKI/Ho8bGxrj2ye0TQ2ZHR8eUq5LhcFhFRUXKzs5OaJcImQCSh5VMAJijli9frvvvv1/Nzc0yM1VVVSkUCqm5uVk1NTWxU+A9PT0aGBiYMmS2t7dr5cqVU7YvWbJEOTk5yS4DwP8pQiYAzGHbtm3TkiVLtH37du3atUvnn3++Nm/erKeeeiq2T0dHh6TEVcm+vj719fWprKws4XXD4XDC9zcBwCWukwkAAADn+E4mAAAAnCNkAgAAwDlCJgAAAJwjZAIAAMA5QiYAAACcI2QCAADAOUImAAAAnCNkAgAAwDlCJgAAAJwjZAIAAMA5QiYAAACcI2QCAADAuf8A4HZqezfu9TgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "\n",
    "data = np.load('contour_data_wcdm_21.npz')\n",
    "\n",
    "\n",
    "OM_grid2 = data['OM_grid2']\n",
    "h_grid2 = data['h_grid2']\n",
    "Z_2 = data['Z_2']\n",
    "\n",
    "\n",
    "OM_grid8 = data['OM_grid8']\n",
    "h_grid8 = data['h_grid8']\n",
    "Z_8 = data['Z_8']\n",
    "\n",
    "\n",
    "levels2 = [\n",
    "    chi2_wcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "levels8 = [\n",
    "    chi2_wwcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wwcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wwcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "\n",
    "contour2 = ax.contour(OM_grid2, h_grid2, Z_2, levels=levels2, colors='red', linewidths=0.7)\n",
    "\n",
    "\n",
    "contour8 = ax.contour(OM_grid8, h_grid8, Z_8, levels=levels8, colors='blue', linewidths=0.7)\n",
    "\n",
    "ax.plot(omwcdm, hwcdm, 'ko', markersize=8) \n",
    "ax.plot(omwwcdm, hwwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r'h')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "\n",
    "custom_lines = [Line2D([0], [0], color='red', lw=2),\n",
    "                Line2D([0], [0], color='blue', lw=2)]\n",
    "ax.legend(custom_lines, ['Pantheon+CMB+BAO+Riess', 'Pantheon+CMB+BAO'], loc='best', fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "feb9228c-bda4-4821-8ce3-5ff4a3214542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "\n",
    "data = np.load('contour_data_wcdm_21.npz')\n",
    "\n",
    "\n",
    "M_grid3 = data['M_grid3']\n",
    "h_grid3 = data['h_grid3']\n",
    "Z_3 = data['Z_3']\n",
    "\n",
    "\n",
    "M_grid9 = data['M_grid9']\n",
    "h_grid9 = data['h_grid9']\n",
    "Z_9 = data['Z_9']\n",
    "\n",
    "\n",
    "levels3 = [\n",
    "    chi2_wcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "levels9 = [\n",
    "    chi2_wwcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wwcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wwcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "\n",
    "contour3 = ax.contour(M_grid3, h_grid3, Z_3, levels=levels3, colors='red', linewidths=0.7)\n",
    "\n",
    "\n",
    "contour9 = ax.contour(M_grid9, h_grid9, Z_9, levels=levels9, colors='blue', linewidths=0.7)\n",
    "\n",
    "ax.plot(Mwcdm, hwcdm, 'ko', markersize=8) \n",
    "ax.plot(Mwwcdm, hwwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax.set_xlabel(r'M') \n",
    "ax.set_ylabel(r'h')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "\n",
    "custom_lines = [Line2D([0], [0], color='red', lw=2),\n",
    "                Line2D([0], [0], color='blue', lw=2)]\n",
    "ax.legend(custom_lines, ['Pantheon+CMB+BAO+Riess', 'Pantheon+CMB+BAO'], loc='best', fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cf72b863-93d6-4f1d-b776-23be1ffa17c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "\n",
    "data = np.load('contour_data_wcdm_21.npz')\n",
    "\n",
    "\n",
    "OM_grid4 = data['OM_grid4']\n",
    "wo_grid4 = data['wo_grid4']\n",
    "Z_4 = data['Z_4']\n",
    "\n",
    "\n",
    "OM_grid10 = data['OM_grid10']\n",
    "wo_grid10 = data['wo_grid10']\n",
    "Z_10 = data['Z_10']\n",
    "\n",
    "\n",
    "levels4 = [\n",
    "    chi2_wcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "levels10 = [\n",
    "    chi2_wwcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wwcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wwcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "\n",
    "contour4 = ax.contour(OM_grid4, wo_grid4, Z_4, levels=levels4, colors='red', linewidths=0.7)\n",
    "\n",
    "\n",
    "contour10 = ax.contour(OM_grid10, wo_grid10, Z_10, levels=levels10, colors='blue', linewidths=0.7)\n",
    "\n",
    "ax.plot(omwcdm, wowcdm, 'ko', markersize=8) \n",
    "ax.plot(omwwcdm, wowwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r'$w_{0}$')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "\n",
    "custom_lines = [Line2D([0], [0], color='red', lw=2),\n",
    "                Line2D([0], [0], color='blue', lw=2)]\n",
    "ax.legend(custom_lines, ['Pantheon+CMB+BAO+Riess', 'Pantheon+CMB+BAO'], loc='best', fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bd3a259b-4337-4625-9e55-14b13520ff7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "\n",
    "data = np.load('contour_data_wcdm_21.npz')\n",
    "\n",
    "\n",
    "M_grid5 = data['M_grid5']\n",
    "wo_grid5 = data['wo_grid5']\n",
    "Z_5 = data['Z_5']\n",
    "\n",
    "\n",
    "M_grid11 = data['M_grid11']\n",
    "wo_grid11 = data['wo_grid11']\n",
    "Z_11 = data['Z_11']\n",
    "\n",
    "\n",
    "levels5 = [\n",
    "    chi2_wcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "levels11 = [\n",
    "    chi2_wwcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wwcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wwcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "\n",
    "contour5 = ax.contour(M_grid5, wo_grid5, Z_5, levels=levels5, colors='red', linewidths=0.7)\n",
    "\n",
    "\n",
    "contour11 = ax.contour(M_grid11, wo_grid11, Z_11, levels=levels11, colors='blue', linewidths=0.7)\n",
    "\n",
    "ax.plot(Mwcdm, wowcdm, 'ko', markersize=8) \n",
    "ax.plot(Mwwcdm, wowwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax.set_xlabel(r'Μ') \n",
    "ax.set_ylabel(r'$w_{0}$')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "\n",
    "custom_lines = [Line2D([0], [0], color='red', lw=2),\n",
    "                Line2D([0], [0], color='blue', lw=2)]\n",
    "ax.legend(custom_lines, ['Pantheon+CMB+BAO+Riess', 'Pantheon+CMB+BAO'], loc='best', fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "65c6e351-c8d0-4e43-86e4-a536a1c2fa03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "\n",
    "data = np.load('contour_data_wcdm_21.npz')\n",
    "\n",
    "\n",
    "h_grid6 = data['h_grid6']\n",
    "wo_grid6 = data['wo_grid6']\n",
    "Z_6 = data['Z_6']\n",
    "\n",
    "\n",
    "h_grid12 = data['h_grid12']\n",
    "wo_grid12 = data['wo_grid12']\n",
    "Z_12 = data['Z_12']\n",
    "\n",
    "\n",
    "levels6 = [\n",
    "    chi2_wcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "levels12 = [\n",
    "    chi2_wwcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wwcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wwcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "\n",
    "contour6 = ax.contour(h_grid6, wo_grid6, Z_6, levels=levels6, colors='red', linewidths=0.7)\n",
    "\n",
    "\n",
    "contour12 = ax.contour(h_grid12, wo_grid12, Z_12, levels=levels12, colors='blue', linewidths=0.7)\n",
    "\n",
    "ax.plot(hwcdm, wowcdm, 'ko', markersize=8) \n",
    "ax.plot(hwwcdm, wowwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax.set_xlabel(r'h') \n",
    "ax.set_ylabel(r'$w_{0}$')\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "\n",
    "custom_lines = [Line2D([0], [0], color='red', lw=2),\n",
    "                Line2D([0], [0], color='blue', lw=2)]\n",
    "ax.legend(custom_lines, ['Pantheon+CMB+BAO+Riess', 'Pantheon+CMB+BAO'], loc='best', fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "892f4c1b-6a7c-4ef9-8c48-d9f145902c40",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "\n",
    "data = np.load('contour_data_wcdm_21.npz')\n",
    "\n",
    "\n",
    "OM_grid1 = data['OM_grid1']\n",
    "M_grid1 = data['M_grid1']\n",
    "Z_1 = data['Z_1']\n",
    "\n",
    "\n",
    "OM_grid7 = data['OM_grid7']\n",
    "M_grid7 = data['M_grid7']\n",
    "Z_7 = data['Z_7']\n",
    "\n",
    "\n",
    "levels1 = [\n",
    "    chi2_wcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "levels7 = [\n",
    "    chi2_wwcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wwcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wwcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "\n",
    "\n",
    "contour1 = ax.contour(OM_grid1, M_grid1, Z_1, levels=levels1, colors='red', linewidths=0.7)\n",
    "\n",
    "\n",
    "contour7 = ax.contour(OM_grid7, M_grid7, Z_7, levels=levels7, colors='blue', linewidths=0.7)\n",
    "\n",
    "ax.plot(omwcdm, Mwcdm, 'ko', markersize=8) \n",
    "ax.plot(omwwcdm, Mwwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax.set_ylabel(r'M')\n",
    "ax.axhline(y=-19.4, color='black', linestyle='--')\n",
    "\n",
    "\n",
    "ax.axhline(y=-19.244, color='black', linestyle='--')\n",
    "ax.text(0.95, -19.4, r'$M_B^{P18} = -19.4$', color='black', \n",
    "        transform=ax.get_yaxis_transform(), va='bottom', ha='right', fontsize=12)\n",
    "\n",
    "ax.text(0.95, -19.244, r'$M_B^{R20} = -19.244$', color='black', \n",
    "        transform=ax.get_yaxis_transform(), va='bottom', ha='right', fontsize=12)\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_edgecolor('black')\n",
    "    spine.set_linewidth(1.5)\n",
    "\n",
    "ax.set_ylim(-19.45, -19.2)\n",
    "ax.set_xlim(0.29, 0.33)\n",
    "custom_lines = [Line2D([0], [0], color='red',  lw=2),\n",
    "                Line2D([0], [0], color='blue',  lw=2)]\n",
    "ax.legend(custom_lines, ['Pantheon+CMB+BAO+Riess', 'Pantheon+CMB+BAO'], loc='best', fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "24a98068-e96d-4b48-8fea-a3554e9bd93c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ενωση αρχειων\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.ticker import MaxNLocator, FormatStrFormatter\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "# Φόρτωση δεδομένων\n",
    "data = np.load('contour_data_wcdm_21.npz')\n",
    "\n",
    "# Δεδομένα για το 1ο γράφημα (Ω_0m vs h)\n",
    "OM_grid2 = data['OM_grid2']\n",
    "h_grid2 = data['h_grid2']\n",
    "Z_2 = data['Z_2']\n",
    "\n",
    "OM_grid8 = data['OM_grid8']\n",
    "h_grid8 = data['h_grid8']\n",
    "Z_8 = data['Z_8']\n",
    "\n",
    "# Δεδομένα για το 2ο γράφημα (M vs h)\n",
    "M_grid3 = data['M_grid3']\n",
    "h_grid3 = data['h_grid3']\n",
    "Z_3 = data['Z_3']\n",
    "\n",
    "M_grid9 = data['M_grid9']\n",
    "h_grid9 = data['h_grid9']\n",
    "Z_9 = data['Z_9']\n",
    "\n",
    "# Επίπεδα (Σημείωση: τα chi2_wcdm, chi2_wwcdm και dchi πρέπει να έχουν οριστεί προηγουμένως)\n",
    "levels_wcdm = [\n",
    "    chi2_wcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "levels_wwcdm = [\n",
    "    chi2_wwcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wwcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wwcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "# Δημιουργία σχήματος με 2 υπογραφήματα\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 6), sharey=True)\n",
    "\n",
    "# --- 1ο Γράφημα (Ω_0m vs h) ---\n",
    "contour2 = ax1.contour(OM_grid2, h_grid2, Z_2, levels=levels_wcdm, colors='red', linewidths=0.7)\n",
    "contour8 = ax1.contour(OM_grid8, h_grid8, Z_8, levels=levels_wwcdm, colors='blue', linewidths=0.7)\n",
    "\n",
    "ax1.plot(omwcdm, hwcdm, 'ko', markersize=8) \n",
    "ax1.plot(omwwcdm, hwwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax1.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax1.set_ylabel(r'h')\n",
    "\n",
    "# Περιορισμός σε 2 δεκαδικά ψηφία και αφαίρεση του τελευταίου αριθμού δεξιά\n",
    "ax1.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax1.xaxis.set_major_locator(MaxNLocator(prune='upper', nbins=5))\n",
    "\n",
    "# --- 2ο Γράφημα (M vs h) ---\n",
    "contour3 = ax2.contour(M_grid3, h_grid3, Z_3, levels=levels3, colors='red', linewidths=0.7)\n",
    "contour9 = ax2.contour(M_grid9, h_grid9, Z_9, levels=levels9, colors='blue', linewidths=0.7)\n",
    "\n",
    "ax2.plot(Mwcdm, hwcdm, 'ko', markersize=8) \n",
    "ax2.plot(Mwwcdm, hwwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax2.set_xlabel(r'M') \n",
    "\n",
    "# Περιορισμός σε 2 δεκαδικά ψηφία και αφαίρεση του πρώτου αριθμού αριστερά\n",
    "ax2.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax2.xaxis.set_major_locator(MaxNLocator(prune='lower', nbins=5))\n",
    "\n",
    "# Μορφοποίηση πλαισίων (spines) και για τα δύο γραφήματα\n",
    "for ax in (ax1, ax2):\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_edgecolor('black')\n",
    "        spine.set_linewidth(1.5)\n",
    "\n",
    "# Προσθήκη υπομνήματος (legend) στο πρώτο γράφημα\n",
    "custom_lines = [Line2D([0], [0], color='red', lw=2),\n",
    "                Line2D([0], [0], color='blue', lw=2)]\n",
    "\n",
    "ax2.legend(custom_lines, ['Pantheon+CMB+BAO+Riess', 'Pantheon+CMB+BAO'], loc='best', fontsize=12)\n",
    "# Κρύψιμο των αριθμών του άξονα y στο δεύτερο γράφημα\n",
    "ax2.tick_params(labelleft=False)\n",
    "ax2.set_xlim(-19.45, -19.35)\n",
    "# Ένωση των γραφημάτων χωρίς κενό\n",
    "plt.subplots_adjust(wspace=0)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1c087c7b-2c77-4822-8d98-e10076aa578c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.ticker import MaxNLocator, FormatStrFormatter\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'serif',\n",
    "    'font.serif': ['Times New Roman'],\n",
    "    'font.size': 18\n",
    "})\n",
    "\n",
    "# Φόρτωση δεδομένων\n",
    "data = np.load('contour_data_wcdm_21.npz')\n",
    "\n",
    "# Δεδομένα 1ου γραφήματος (Ω_0m vs w_0)\n",
    "OM_grid4 = data['OM_grid4']\n",
    "wo_grid4 = data['wo_grid4']\n",
    "Z_4 = data['Z_4']\n",
    "\n",
    "OM_grid10 = data['OM_grid10']\n",
    "wo_grid10 = data['wo_grid10']\n",
    "Z_10 = data['Z_10']\n",
    "\n",
    "# Δεδομένα 2ου γραφήματος (M vs w_0)\n",
    "M_grid5 = data['M_grid5']\n",
    "wo_grid5 = data['wo_grid5']\n",
    "Z_5 = data['Z_5']\n",
    "\n",
    "M_grid11 = data['M_grid11']\n",
    "wo_grid11 = data['wo_grid11']\n",
    "Z_11 = data['Z_11']\n",
    "\n",
    "# Δεδομένα 3ου γραφήματος (h vs w_0)\n",
    "h_grid6 = data['h_grid6']\n",
    "wo_grid6 = data['wo_grid6']\n",
    "Z_6 = data['Z_6']\n",
    "\n",
    "h_grid12 = data['h_grid12']\n",
    "wo_grid12 = data['wo_grid12']\n",
    "Z_12 = data['Z_12']\n",
    "\n",
    "# Επίπεδα (τα chi2_wcdm, chi2_wwcdm και dchi πρέπει να έχουν οριστεί προηγουμένως)\n",
    "levels_wcdm = [\n",
    "    chi2_wcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "levels_wwcdm = [\n",
    "    chi2_wwcdm + dchi(1,2),  # 1 sigma \n",
    "    chi2_wwcdm + dchi(2,2),  # 2 sigma \n",
    "    chi2_wwcdm + dchi(3,2)   # 3 sigma \n",
    "]\n",
    "\n",
    "# Δημιουργία σχήματος με 3 υπογραφήματα\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(16, 6), sharey=True)\n",
    "\n",
    "# --- 1ο Γράφημα (Ω_0m vs w_0) ---\n",
    "contour4 = ax1.contour(OM_grid4, wo_grid4, Z_4, levels=levels_wcdm, colors='red', linewidths=0.7)\n",
    "contour10 = ax1.contour(OM_grid10, wo_grid10, Z_10, levels=levels_wwcdm, colors='blue', linewidths=0.7)\n",
    "\n",
    "ax1.plot(omwcdm, wowcdm, 'ko', markersize=8) \n",
    "ax1.plot(omwwcdm, wowwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax1.set_xlabel(r'$\\Omega_{0m}$') \n",
    "ax1.set_ylabel(r'$w_{0}$')\n",
    "\n",
    "ax1.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax1.xaxis.set_major_locator(MaxNLocator(prune='upper', nbins=5))\n",
    "\n",
    "# --- 2ο Γράφημα (M vs w_0) ---\n",
    "contour5 = ax2.contour(M_grid5, wo_grid5, Z_5, levels=levels_wcdm, colors='red', linewidths=0.7)\n",
    "contour11 = ax2.contour(M_grid11, wo_grid11, Z_11, levels=levels_wwcdm, colors='blue', linewidths=0.7)\n",
    "\n",
    "ax2.plot(Mwcdm, wowcdm, 'ko', markersize=8) \n",
    "ax2.plot(Mwwcdm, wowwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax2.set_xlabel(r'M') \n",
    "\n",
    "ax2.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax2.xaxis.set_major_locator(MaxNLocator(prune='upper', nbins=5))\n",
    "\n",
    "# --- 3ο Γράφημα (h vs w_0) ---\n",
    "contour6 = ax3.contour(h_grid6, wo_grid6, Z_6, levels=levels_wcdm, colors='red', linewidths=0.7)\n",
    "contour12 = ax3.contour(h_grid12, wo_grid12, Z_12, levels=levels_wwcdm, colors='blue', linewidths=0.7)\n",
    "\n",
    "ax3.plot(hwcdm, wowcdm, 'ko', markersize=8) \n",
    "ax3.plot(hwwcdm, wowwcdm, 'ko', markersize=10) \n",
    "\n",
    "ax3.set_xlabel(r'h') \n",
    "\n",
    "ax3.xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "ax3.xaxis.set_major_locator(MaxNLocator(nbins=5)) # Εδώ δεν χρειάζεται prune γιατί είναι το τελευταίο δεξιά\n",
    "\n",
    "# Μορφοποίηση πλαισίων (spines) και για τα τρία γραφήματα\n",
    "for ax in (ax1, ax2, ax3):\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_edgecolor('black')\n",
    "        spine.set_linewidth(1.5)\n",
    "\n",
    "# Προσθήκη υπομνήματος (legend) στο πρώτο γράφημα\n",
    "custom_lines = [Line2D([0], [0], color='red', lw=2),\n",
    "                Line2D([0], [0], color='blue', lw=2)]\n",
    "ax3.legend(custom_lines, ['Pantheon+CMB+BAO+Riess', 'Pantheon+CMB+BAO'], loc='best', fontsize=12)\n",
    "\n",
    "ax.set_ylim(-1.2, -0.9)\n",
    "ax2.set_xlim(-19.45, -19.35)\n",
    "# Κρύψιμο των αριθμών του άξονα y στο δεύτερο και τρίτο γράφημα\n",
    "ax2.tick_params(labelleft=False)\n",
    "ax3.tick_params(labelleft=False)\n",
    "\n",
    "# Ένωση των γραφημάτων χωρίς κενό\n",
    "plt.subplots_adjust(wspace=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d13d7e8-e6d0-4c79-81fa-ab64f3117bc8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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